Patients living with disabilities
Bibliographic record
Abstract
Objective To compare the potential risk factors for lower-quality primary care, the potential markers of unmet needs in primary care, and the willingness to participate in future research among primary care patients with versus without physical disabilities. Design A waiting room survey using a convenience sample. Setting A family health team (FHT) in Kitchener-Waterloo, Ont, with a designated Mobility Clinic. Participants A total of 40 patients seen at the FHT Mobility Clinic and 80 patients from the general patient population of the same FHT. Main outcome measures Socioeconomic status and social capital, number of self-reported emergency department visits and hospitalizations in the preceding year, and willingness of the patients in the 2 groups to participate in future research studies. Results Patients from the Mobility Clinic were more than twice as likely to be receiving benefits or social assistance (75.0% vs 32.1%, P < .001), were twice as likely to report an annual household income of less than $40000 (58.6% vs 29.2%, P = .006), and were more likely to report their health status to be fair or poor (42.5% vs 16.2%, P = .002). Half of Mobility Clinic patients had visited the emergency department at least once in the preceding year, compared with 29.7% in the general patient population ( P = .027). When asked if they would be willing to provide their health card number in the future so that it could be linked to health care data for research, 82.5% of Mobility Clinic patients agreed versus 55.0% of those in the general patient population ( P = .004). Conclusion In this study, patients with disabilities were at a social disadvantage compared with their peers without disabilities and were more likely to use the emergency department, suggesting that they had unmet health needs. Future research should continue to explore this patient population and to investigate if an interprofessional primary health care team approach focused on patients with disabilities can help to increase quality of care.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 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".