Potential harms associated with routine collection of patient sociodemographic information: A rapid review
Bibliographic record
Abstract
BACKGROUND: Health systems are recommended to capture routine patient sociodemographic data as a key step in providing equitable person-centred care. However, collection of this information has the potential to cause harm, especially for vulnerable or potentially disadvantaged patients. OBJECTIVE: To identify harms perceived or experienced by patients, their families, or health-care providers from collection of sociodemographic information during routine health-care visits and to identify best practices for when, by whom and how to collect this information. SEARCH STRATEGY: We searched OVID MEDLINE, PubMed "related articles" via NLM and healthevidence.org to the end of January 2018 and assessed reference lists and related citations of included studies. INCLUSION CRITERIA: We included studies reporting on harms of collecting patient sociodemographic information in health-care settings. DATA EXTRACTION AND SYNTHESIS: Data on study characteristics and types of harms were extracted and summarized narratively. MAIN RESULTS: Eighteen studies were included; 13 provided patient perceptions or experiences with the collection of these data and seven studies reported on provider perceptions. Five reported on patient recommendations for collecting sociodemographic information. Patients and providers reported similar potential harms which were grouped into the following themes: altered behaviour which may affect care-seeking, data misuse or privacy concerns, discomfort, discrimination, offence or negative reactions, and quality of care. Patients suggested that sociodemographic information be collected face to face by a physician. DISCUSSION AND CONCLUSIONS: Overall, patients support the collection of sociodemographic information. However, harms are possible, especially for some population subgroups. Harms may be mitigated by providing a rationale for the collection of this information.
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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 | 0.028 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".