Understanding patient preferences on providing sociodemographic information in an acute care setting: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Access to patient sociodemographic information is a critical factor to understanding, at a systems-level, where health inequities exist so they can be addressed. Patient concerns around disclosing personal information remain a barrier to sociodemographic data collection. Our objective was to assess patient perceptions regarding the routine collection of sociodemographic information in an acute care hospital setting. METHODS: We conducted a qualitative study using the Framework Method to understand patient perceptions regarding sociodemographic data collection. We administered semi-structured interviews with patients admitted to the General Internal Medicine and Geriatric Medicine units at a university-affiliated hospital in Toronto, Canada. Two reviewers independently coded 10% of interview transcripts until a kappa ≥ 0.7 was achieved. The remaining interviews were single-coded. Data were analyzed using thematic analysis. RESULTS: A total of 52 qualitative interviews were conducted between December 2021 and September 2023. Of the 52 individuals interviewed, 21 also agreed to complete a sociodemographic survey (40%). Among these participants, 57% (n = 12) were women and 62% (n = 13) were White. Patients felt more comfortable disclosing sociodemographic data if they believed it would lead to better or more equitable care; data were collected after they were admitted to hospital; data were collected verbally; and concerns about data privacy, anonymization, use, and secure storage were addressed. Some patients expressed discomfort being asked questions about income or race. Most patients had no preference regarding who from the healthcare team collected the sociodemographic data. Participants reported the importance of a friendly and respectful approach of the person collecting the data. CONCLUSIONS: Participants reported feeling comfortable disclosing their sociodemographic information in hospital; however, only 40% were willing to complete a demographic questionnaire. Participants’ comfort levels were impacted by the approach of the individual asking the questions, the types of questions asked, when the data were collected, and whether assurances around privacy and transparency regarding the use of data were provided. The results of this study should be used to develop strategies to support the implementation of routine sociodemographic data collection in acute care settings.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".