The representation of vulnerable populations in quality improvement studies
Bibliographic record
Abstract
PURPOSE: A mapping review to quantify representation of vulnerable populations, who suffer from disparity and often inequitable healthcare, in quality improvement (QI) research. DATA SOURCES: Studies published in 2004-2014 inclusive from Medline, Embase and Cochrane databases for English language research with the terms 'quality improvement' or 'quality control' or 'QI' and 'plan-do-study-act' or 'PDSA' in the years 2004-2014 inclusively. STUDY SELECTION: Published clinical research that was a QI-themed, as identified by its declared search terms, MESH terms, abstract or title. DATA EXTRACTION: Three reviewers identified the eligible studies independently. Excluded were publications that were not trials, evaluations or analyses. RESULTS OF DATA SYNTHESIS: Of 2039 results, 1660 were eligible for inclusion. There were 586 (33.5%) publications that targeted a specific vulnerable population: children (184, 10.54%), mental health patients (125, 7.16%), the elderly (100, 5.73%), women (57, 3.27%), the poor (30, 1.72%), rural residents (29, 1.66%), visible minorities (27, 1.55%), the terminally ill (17, 0.97%), adolescents (16, 0.92%) and prisoners (1 study). Seventy-four articles targeted two or more vulnerable populations, and 11 targeted three population categories. On average, there were 158 QI research studies published per year, increasing from 69 in 2004 to 396 in 2014 (R2 = 0.7, P < 0.001). The relative representation of vulnerable populations had a mean of 33.58% and was stable over the time period (standard deviation (SD) = 5.9%, R2 = 0.001). Seven countries contributed to over 85% of the publications targeting vulnerable populations, with the USA contributing 62% of the studies. CONCLUSIONS: Over 11 years, there has been a marked increase in QI publications. Roughly one-third of all published QI research is on vulnerable populations, a stable proportion over time. Nevertheless, some vulnerable populations are under-represented. Increased education, resources and attention are encouraged to improve the health of vulnerable populations through focused QI initiatives.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
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 | 0.176 | 0.510 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.033 | 0.033 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".