Collecting sociodemographic data in primary care: qualitative interviews in community health centres
Bibliographic record
Abstract
BACKGROUND: Many primary care organisations do not routinely collect sociodemographic data (SDD), such as race, sex, or income, despite the importance of these data in addressing health disparities. AIM: To understand the experiences of primary care providers and staff in collecting SDD. DESIGN & SETTING: A qualitative interview study with 33 primary care and interprofessional team members from eight Ontario community health centres (CHCs). METHOD: Semi-structured virtual interviews were conducted between July and August 2021. The interviews were recorded and transcribed verbatim. Content analysis of the transcripts was undertaken. RESULTS: Participants reported using both formal methods of SDD collection, and informal methods of SDD collection that were more organic, varied, and conducted over time. Participants discussed sometimes feeling uncomfortable collecting SDD formally, as well as associated burden and limited resources to support collection. Client-provider rapport was noted as facilitating data collection and participants suggested more training, streamlined data collection, and better communication about purpose and use of data. CONCLUSION: SDD can be collected informally or formally, but there are limitations to informally collected data and barriers to the adoption of formal processes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".