Reaching those most in need: A scoping review of interventions to improve health care quality for disadvantaged populations with osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: To conduct a systematic review to identify and describe the scope and nature of the research evidence on the effectiveness of interventions to improve health care quality or reduce disparities in the care of disadvantaged populations with osteoarthritis (OA) as an example of a common chronic disease. METHODS: We searched electronic databases from 1950 through February 2010 and grey literature for relevant articles using any study design. Studies with interventions designed explicitly to improve health care quality or reduce disparities in the care of disadvantaged adult populations with OA and including an evaluation were eligible. We used the PROGRESS-Plus framework to identify disadvantaged population subgroups. RESULTS: Of 4,701 citations identified, 10 met the inclusion criteria. Eight were community based and 6 targeted race/ethnicity/culture. All 10 studies evaluated interventions aimed at people with OA; 2 studies also targeted the health care system. No studies targeted health care providers. Nine of 10 studies evaluated arthritis self-management interventions; all showed some benefit. Only 1 study compared the difference in effect between the PROGRESS-Plus disadvantaged population and the relevant comparator group. CONCLUSION: There are few studies evaluating the effectiveness of interventions to improve health care quality in disadvantaged populations with OA. Further research is needed to evaluate interventions aimed at health care providers and the health care system, as well as other patient-level interventions. Gap intervention research is also needed to evaluate whether interventions are effective in reducing documented health care inequities.
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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 | 0.029 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".