Canadian Inpatient Orthogeriatric Models of Care: A Mixed Methods Survey of Facilitators and Barriers
Bibliographic record
Abstract
Background: Fragility fractures are a serious and common consequence of falls in older adults. Orthogeriatric models of care reduce mortality and morbidity, but, despite this evidence, orthogeriatric programs (OGPs) are not standardized across Canada. The aim of this study was to better understand the facilitators and barriers of OGPs across Canada. Methods: software. Results: 62 CGS members completed the survey. Respondents came from nine provinces/territories, with most being physicians from academic centres. 77% respondents indicated an existing OGP at their site, commonly an optional or automatic geriatrician consult. 23% indicated no formal OGP, of which 56% had an alternative service automatically consulted for older adults with fragility fracture, commonly internal medicine or a hospitalist. Responders indicated the most important factor in establishing an OGP is clinical leadership (56%, 10/18), and the most common barriers are lack of hospital prioritization and lack of funding (41%, 62/153). Conclusions: The survey found that clinical leadership, hospital prioritization, and available funding are imperative to establishing OGPs. Limitations include the survey being distributed only to CGS members, a lower response rate, and respondents predominantly from academic centres in Ontario. Further qualitative data from other specialties (for example, orthopedics) and greater representation from community hospitals would be helpful to understand additional perceived barriers and facilitators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".