Medical directors of long-term care facilities: preventing another physician shortage?
Bibliographic record
Abstract
OBJECTIVE: The long-term care (LTC) sector in Canada is expanding, but little attention has been given to medical human resources in this area. Our objective was to seek LTC medical directors' opinions about medical services in LTC and about strategies for recruitment and retention. DESIGN: Mailed survey. SETTING: Long-term care facilities and nursing homes. PARTICIPANTS: Seven hundred five medical directors of LTC facilities across Canada were identified from the Canadian Healthcare Association database. MAIN OUTCOME MEASURES: Responses to open- and closed-ended questions and to Likert-type scales. RESULTS: The response rate was 55%. The average age of medical directors was 54 years. Most had started work in LTC because of a vacant position, as opposed to self-perceived skills or training. Most (75.3%) reported satisfaction with their role as medical directors, but 82.7% believed that there was a significant shortage of physicians working in LTC, and 42% had seriously considered leaving their positions. Major sources of satisfaction identified were clinical, especially working with older patients and improving care. Important sources of dissatisfaction were remuneration for LTC work, on-call coverage, and excessive paperwork. Directors suggested increases to fee schedules as the main recruitment and retention strategy, and many believed that increasing exposure to LTC during residency would increase recruitment. Development of larger on-call groups for coverage and alternative methods of remuneration were not cited as important factors. Most did not believe that working in a teaching nursing home would increase their satisfaction. Directors did not think the use of nurse practitioners would alleviate concerns about shortages of physicians. CONCLUSION: Medical directors of LTC facilities are aging, and many are considering leaving their work in LTC. Without an increase in the number of physicians willing to work in LTC institutions, the current shortage of LTC physicians could increase in the near future. Medical directors' responses to questions could help guide strategies to recruit and retain physicians. Future areas of research should include the perspectives of physicians who are not medical directors and of family medicine residents.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".