“People Need to Know; We’re Part of the Community. We’re Here.”: Examining Experiences of Sharing Demographic Information for a Community-Based Diabetes Prevention Program
Bibliographic record
Abstract
BackgroundCollecting demographic data is critical for identifying inequities in healthcare services and delivery. Inaccurate collection of demographic data can make developing equitable health interventions and improving reach of existing interventions difficult. This study aimed to (a) examine experiences in completing a community-based type 2 diabetes prevention program Small Steps for Big Changes (SSBC) demographic questionnaire (SSBC-DQ) among adults from equity-owed groups, and (b) assess recommendations for improvement to the questionnaire.MethodsAdults with no prior involvement in SSBC were recruited. Participants completed the SSBC-DQ online and then engaged in one-on-one structured interviews. Interview data was analyzed using interpretive description and coded using the APEASE criteria.ResultsTwelve participant interviews were included in analysis. Five principle themes were developed to capture the experiences of completing the SSBC-DQ: representation, comprehension, demographics are an emotional experience, the role that privilege plays, and beliefs about demographic data. Sixty suggested changes were coded using the APEASE criteria; six suggestions met the criteria for implementation, 20 did not meet the criteria, and 34 required further discussion with the research team.ConclusionsResults from this study illustrate that people's lived experiences can drive their reactions and interpretations to demographic questionnaires. Based on end-user suggestions, SSBC made changes to its demographic questionnaire to be more inclusive. Having a demographic questionnaire that is more inclusive can help SSBC better understand what populations it is and is not reaching in an acceptable and inclusive manner. This will help inform future directions regarding evaluating program reach and equity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".