The research activities of Ontario’s large community hospitals: an updated scoping review
Bibliographic record
Abstract
BACKGROUND: Community hospitals provide the majority of patient care in Canada but traditionally do not participate in clinical research. The disconnect between where most patients receive their health care and where health research is conducted leads to decreased study recruitment, reduced generalizability of study results, and inequitable patient access to novel therapies. A scoping review of the research activities of Ontario's large community hospitals (LCHs) between 2013 and 2015 reported an annualized output of 266 publications. In the last decade, efforts have been made to engage more community hospitals in research. In this updated scoping review, we provide a snapshot of the research activities of Ontario's LCHs between 2016 and 2022, describing the number and type of research publications as well as the frequency of collaboration within and between LCHs. METHODS: Three medical databases (PubMed, Embase, and CINAHL) were searched for publications that included at least one author affiliated with one of Ontario's 47 LCHs, and for which the topic was hospital or health related. Screening and extraction occurred concurrently. RESULTS: We identified 3,719 publications from 2016 to 22 with at least one Ontario LCH-affiliated author, representing an annualized output of 531 publications. The most frequent publication type was observational study (n = 1,654; 45%), quality improvement (n = 355; 10%), systematic reviews (n = 352; 9%) and randomized controlled trials (n = 325; 9%). The most common disciplines were outpatient care (n = 1,144; 31%), health systems research (n = 806; 22%), inpatient care (n = 437; 12%) and surgery (n = 403; 11%). LCH-affiliated first authors were identified in 997 (27%) publications, representing 755 unique authors, while LCH-affiliated senior authors were identified in 962 (26%) publications, representing 583 unique authors. Among the 1,565 studies with an LCH-affiliated first or senior author, 574 (37%) included collaborators from the same LCH and 86 (5%) included collaborators from other Ontario LCHs. CONCLUSIONS: Health research by LCH-affiliated clinicians and researchers increased significantly in 2016-2022 relative to 2013-2015. Participation in randomized controlled trials however, remains low, suggesting that further efforts are required to build clinical research infrastructure in LCHs.
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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 | 0.049 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.071 | 0.105 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".