This readme file was generated on 2022-08-25 by Montserrat Rue GENERAL INFORMATION Title of Dataset: InforMa-data Author/Principal Investigator Information Name: Montserrat Rue ORCID: 0000-0002-7862-9365 Institution: University of Lleida Address: Rovira Roure 80, IRBLleida Email: montserrat.rue@udl.cat Author/Associate or Co-investigator Information Name: Montserrat Martínez-Alonso ORCID: 0000-0003-1504-8552 Institution: University of Lleida Address: Rovira Roure 80, IRBLleida Email: montserrat.martinez@udl.cat Date of data collection: The fieldwork was conducted between July 1, 2016 and September 14, 2017. The trial ended when the sample size for the primary and secondary outcomes was achieved. Follow-up for assessment of participation in the BCSP was closed on June 20, 2018. Geographic location of data collection: Four breast cancer screening programs (BCSPs) of the Spanish public health system participated in the study. The participant BCSPs are managed by Hospital del Mar in Barcelona, the Cancer Prevention and Control Program of the Catalan Institute of Oncology, the Canary Islands Health Service, and the Lleida Health Region. Information about funding sources that supported the collection of the data: This study was supported by the research grant “Women participation in decisions and strategies on early detection of breast cancer” (PI14/00113) from the Instituto de Salud Carlos III and cofunded by Fondo Europeo de Desarrollo Regional (FEDER) “Una manera de hacer Europa”. Anna Pons received a grant for PhD students from the Lleida Biomedical Research Institute (IRBLLEIDA). The funders did not participate in the study design; collection, management, analysis, and interpretation of data; writing of the report; and the decision to submit the report for publication. SHARING/ACCESS INFORMATION Licenses/restrictions placed on the data: None Links to publications that cite or use the data: - Study protocol published in Trials. Available at https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-017-2161-7 - Study results published in Plos One. Available at https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0214057 Links to other publicly accessible locations of the data: None Links/relationships to ancillary data sets: None Was data derived from another source? No If yes, list source(s): Recommended citation for this dataset: Pérez-Lacasta MJ, Martínez-Alonso M, Garcia M, Sala M, Perestelo-Pérez L, Vidal C, et al. (2019) Effect of information about the benefits and harms of mammography on women’s decision making: The InforMa randomised controlled trial. PLoS ONE 14(3): e0214057. https://doi.org/10.1371/journal.pone.0214057 DATA & FILE OVERVIEW File List: InforMa-data.csv. It contains most of the study data used in the publication above cited. Some variables (e.g. date of birth, date of first visit, ..) have been omitted to prevent participants' identification. Additional related data collected that was not included in the current data package: Some study management data not rellevant for the study objectives. Are there multiple versions of the dataset? No METHODOLOGICAL INFORMATION Description of methods used for collection/generation of data: The methods sections of the protocol and study articles have a detailed description in the above mentioned links. Methods for processing the data: Data was collected with an information system designed for the study. The collected data was exported as csv files. Instrument- or software-specific information needed to interpret the data: The R script for reading and tidying the data is included. It is named InforMa-reading.R. All the necessary packages are R packages that can be downloaded from the R web. Standards and calibration information, if appropriate: NA Environmental/experimental conditions: NA Describe any quality-assurance procedures performed on the data: The information system facilitated the data collection with windows that contained the variable categories or checking the possible ranges for numeric variables. People involved with sample collection, processing, analysis and/or submission: They are mentioned in https://doi.org/10.1371/journal.pone.0214057 DATA-SPECIFIC INFORMATION FOR: [InforMa-data.csv] Number of variables: 119 Number of cases/rows: 400 Variable List: Variable name Type Description internalId numeric ID program character Screening program cluster numeric Cluster: Basic Health Area where women were sampled group character Randomized Controlled Trial arm (Interv.-Control) Pre-intervention questionnaire age numeric Age birthplace character Birth place studies character Education employment character Employment mammoUse character Previous use of screening mammogram childrenNumber numeric Number of children grouped hasFamilyHistory character Family history of breast cancer perceivKnowBenefits likert Perceived knowledge on benefits of breast screening (very bad (1) to very good(5)) perceivKnowHarms likert Perceived knowledge on harms of breast screening (very bad (1) to very good(5)) attitudeBenefits likert General attitudes towards knowing benefits of breast screening (not at all important (1) to very important (5)) attitudeHarms likert General attitudes towards knowing harms of breast screening (not at all important (1) to very important (5)) inten1 likert General intentions about participation on breast screening (Appropriateness, not at all (1) to very much (5)) inten2 likert General intentions about participation on breast screening (Importance, not at all (1) to very much (5)) inten3 likert General intentions about participation on breast screening (Unpleasantness, not at all (5) to very much (1)) scoreGeneralKnowledge numeric Score for general knowledge (pre-intervention) scoreGeneralAttitude numeric Score for general attitude (pre-intervention) scoreGeneralIntention numeric Score for general intention (pre-intervention) Post-intervention questionnaire knowconc1 character Knowledge of the benefits and harms of breast cancer screening (conceptual knowledge 1) Screening is for women without symptoms A1: Correct, A2: Incorrect knowconc2 character Knowledge of the benefits and harms of breast cancer screening (conceptual knowledge 2) Screening will not find every breast cancer A1: Incorrect, A2: Correct knowconc3 character Knowledge of the benefits and harms of breast cancer screening (conceptual knowledge 3) Screening may lead to false positive results A1: Correct, A2: Incorrect knowconc4 character Knowledge of the benefits and harms of breast cancer screening (conceptual knowledge 4) Screening reduces breast cancer deaths A1: Incorrect, A2: Correct knowconc5 character Knowledge of the benefits and harms of breast cancer screening (conceptual knowledge 5) Screening increases breast cancer diagnoses A1: Correct, A2: Incorrect knowconc6 character Knowledge of the benefits and harms of breast cancer screening (conceptual knowledge 6) Not all breast cancers cause illness and death A1: Incorrect, A2: Correct knowconc7 character Knowledge of the benefits and harms of breast cancer screening (conceptual knowledge 7) Cannot predict if a cancer will cause harm A1: Incorrect, A2: Correct knowconc8 character Knowledge of the benefits and harms of breast cancer screening (conceptual knowledge 8) Cancer that might not cause problem is treated A1: Correct, A2: Incorrect knowconc9 character Knowledge of the benefits and harms of breast cancer screening (conceptual knowledge 9) Some women get treatment they do not need A1: Correct, A2: Incorrect knowconc10 character Knowledge of the benefits and harms of breast cancer screening (conceptual knowledge 10) Overdiagnose more often than prevent death A1: Correct, A2: Incorrect knowconc11 character Knowledge of the benefits and harms of breast cancer screening (conceptual knowledge 11) Overdiagnosis vs false positives distinction A1: Correct, A2: Incorrect For the following questions, imagine 200 women getting screening mammograms every 2 years from ages 50 to 69 knownum1 character Knowledge of the benefits and harms of breast cancer screening (numerical knowledge 1) How many deaths from breast cancer are prevented thanks to screening? A1: 0, A2: 1-2, A3: 3, A4: >3, A5: don't know knownum2 character Knowledge of the benefits and harms of breast cancer screening (numerical knowledge 2) How many will die of breast cancer, despite participating in screening? A1: 0, A2: 1, A3: 2-6, A4: 7-10, A5: >10, A6: don't know knownum3 character Knowledge of the benefits and harms of breast cancer screening (numerical knowledge 3) How many will be diagnosed and treated for breast cancer that would never have caused a health problem? A1: 0, A2: 1-4, A3: 5, A4: >5, A5: don't know knownum4 character Knowledge of the benefits and harms of breast cancer screening (numerical knowledge 4) How many will have a false positive? A1: <20, A2: 20-29, A3: 30-50, A4: 51-60, A5: > 60, A6: don't know attitude1 likert Screening attitudes scale (item 1, beneficial: not at all (1) to very much (5)) attitude2 likert Screening attitudes scale (item 2, harmful: very much (1) to not at all (5)) attitude3 likert Screening attitudes scale (item 3, a good thing: not at all (1) to very much (5)) attitude4 likert Screening attitudes scale (item 4, important: not at all (1) to very much (5)) attitude5 likert Screening attitudes scale (item 5, worthwile: not at all (1) to very much (5)) intentscr character Intentions about having breast screening A1: definitely will, A2: likely to, A3: unsure, A4: not likely to, A5: definitely will not dcs1 character Decisional conflict. O'Connor Decisional Conflict scale (item 1, A1: yes, A2: no, A3: not sure) dcs2 character Decisional conflict. O'Connor Decisional Conflict scale (item 2, A1: yes, A2: no, A3: not sure) dcs3 character Decisional conflict. O'Connor Decisional Conflict scale (item 3, A1: yes, A2: no, A3: not sure) dcs4 character Decisional conflict. O'Connor Decisional Conflict scale (item 4, A1: yes, A2: no, A3: not sure) dcs5 character Decisional conflict. O'Connor Decisional Conflict scale (item 5, A1: yes, A2: no, A3: not sure) dcs6 character Decisional conflict. O'Connor Decisional Conflict scale (item 6, A1: yes, A2: no, A3: not sure) dcs7 character Decisional conflict. O'Connor Decisional Conflict scale (item 7, A1: yes, A2: no, A3: not sure) dcs8 character Decisional conflict. O'Connor Decisional Conflict scale (item 8, A1: yes, A2: no, A3: not sure) dcs9 character Decisional conflict. O'Connor Decisional Conflict scale (item 9, A1: yes, A2: no, A3: not sure) dcs10 character Decisional conflict. O'Connor Decisional Conflict scale (item 10, A1: yes, A2: no, A3: not sure) confidence1 likert Confidence in the decision (item 1) How confident do you feel about your decision? not at all (1) to very much (5) confidence2 likert Confidence in the decision (item 2) How confident do you feel of having understood the information in order to make a decision? not at all (1) to very much (5) confidence3 likert Confidence in the decision (item 3) How confident do you feel that you have arrived at the best decision for you? not at all (1) to very much (5) A confidence in the decision score is obtained as the mean of confidence1, confidence2 and confidence3 anxiety1 likert Anxiety about screening participation. Spielberger State Trait Anxiety Inventory (STAI), (item 1 A1: not at all (4) to A4: very much (1)) anxiety2 likert Anxiety about screening participation. Spielberger State Trait Anxiety Inventory (STAI), (item 2 A1: not at all (1) to A4: very much (4)) anxiety3 likert Anxiety about screening participation. Spielberger State Trait Anxiety Inventory (STAI), (item 3 A1: not at all (1) to A4 very much (4)) anxiety4 likert Anxiety about screening participation. Spielberger State Trait Anxiety Inventory (STAI), (item 4 A1: not at all (4) to A4: very much (1)) anxiety5 likert Anxiety about screening participation. Spielberger State Trait Anxiety Inventory (STAI), (item 5 A1: not at all (4) to A4: very much (1)) anxiety6 likert Anxiety about screening participation. Spielberger State Trait Anxiety Inventory (STAI), (item 6 A1: not at all (1) to A4: very much (4)) worry likert Worry about breast cancer, A1: not worried at all, A2: a little worried, A3: quite worried, A4: very worried regret1 likert Might later regret if do not screen, A1: strongly agree to A5: strongly disagree regret2 likert Might later regret if do screen, A1: strongly agree to A5: strongly disagree tempOrient1 likert Temporal orientation. Consideration of future consequences scale (item 1) I think about the future and that influences my current behavior, A1: completely agree (5) to completely disagree (1) tempOrient2 likert Temporal orientation. Consideration of future consequences scale (item 2) I don't think about what might happen in the future, A1: completely agree (1) to completely disagree (5) tempOrient3 likert Temporal orientation. Consideration of future consequences scale (item 3) I am willing to sacrifice myself to be better in the future, A1: completely agree (5) to completely disagree (1) tempOrient4 likert Temporal orientation. Consideration of future consequences scale (item 4) I prefer to think about the present rather than the future, A1: completely agree (1) to completely disagree (5) avoidDeath likert In deciding whether to have screening, how important is it for you to consider the chance of avoideing death from breast cancer A1: very important to A4: not at all important. Same coding fo the following two variables probOverdiag likert In deciding whether to have screening, how important is it for you to consider the chance of overdiagnosis progbFP likert In deciding whether to have screening, how important is it for you to consider the chance of false positives perceivRisc likert Perceived risk of breast cancer, A1: very low, A2: low, A3: moderate, A4: high perceivRiscSelf likert Perceived risk of breast cancer relative to the average screened women , A1: much lower, A2: a bit lower, A3: about the sae, A4: a bit higher, A5: much higher probBenefitifscreened likert Compared with the average screened woman, if you are screened, how likely is it that you would avoid death from breast cancer A1: much less likely to A5: much more likely. Same coding fo the following two variables probOverdiagifscreened likert Compared with the average screened woman, if you are screened, how likely is it that you would avoid overdiagnosis probFPifscreened likert Compared with the average screened woman, if you are screened, how likely is it that you would avoid false positive results bcScreening numeric Points for conceptual knowledge item 1 BC1 numeric Points for conceptual knowledge item 2 (benefit) BC2 numeric Points for conceptual knowledge item 3 (benefit) FC1 numeric Points for conceptual knowledge item 4 (false positives) OC1 numeric Points for conceptual knowledge item 5 (overdiagnosis) OC2 numeric Points for conceptual knowledge item 6 (overdiagnosis) OC3 numeric Points for conceptual knowledge item 7 (overdiagnosis) OC4 numeric Points for conceptual knowledge item 8 (overdiagnosis) OC5 numeric Points for conceptual knowledge item 9 (overdiagnosis) OC6 numeric Points for conceptual knowledge item 10 (overdiagnosis) OC7 numeric Points for conceptual knowledge item 11 (overdiagnosis) concepknowben numeric BC1+BC2 concepknowfp numeric FC1 concepknowover numeric OC1+OC2+OC3+OC4+OC5+OC6+OC7 concepknow numeric Conceptual knowledge score: concepknowben+concepknowfp+concepknowover BN1 numeric Points for numerical knowledge of benefits, item 1 BN2 numeric Points for numerical knowledge of benefits, item 2 ON1 numeric Points for numerical knowledge of overdiagnosis, item 1 FN1 numeric Points for numerical knowledge of false positives, item 1 BX numeric Points for relative numerical knowledge on benefits FX numeric Points for relative knowledge on false positives OX numeric Points for relative knowledge on overdiagnosis numericknowben numeric BN1+BN2+BX numericknowfp numeric FN1+FX numericknowover numeric ON1+OX numericknow numeric Sum of numericknowben, numericknowfp and numericknowover knowbenefit numeric concepknowben + numericknowben knowfp numeric concepknowfp + numericknowfp knowoverdiag numeric concepknowover + numericknowover totalknow numeric concepknow + numericknow meetthresbenefit numeric Meet the treshold of benefit (Hersch et al. Lancet 2015), 1: yes, 0: no meetthresfp numeric Meet the treshold of false positive (Hersch et al. Lancet 2015), 1: yes, 0: no meetthresoverdiag numeric Meet the treshold of overdiagnosis (Hersch et al. Lancet 2015), 1: yes, 0: no adeqknowledge numeric Adequate knowledge (Hersch et al. Lancet 2015), 1: yes, 0: no attitude numeric Attitude score (post-intervention) positiveattitude numeric Positive attitude (post-intervention), 1: yes, 0: no gattitude numeric Attitude score grouped (post-intervention) screenintent numeric Screening intentions, 1: yes, 0: no informedchoice numeric Informed choice, 1: yes, 0: no Missing data codes: NA Specialized formats or other abbreviations used: None