Family medicine anesthesia: sustaining an essential service.
Bibliographic record
Abstract
OBJECTIVE: To elicit the opinions of family physician anesthetists (FPAs) and hospital Chief Executive Officers (CEOs) regarding the structure of their organizations and the importance of family medicine anesthesia. DESIGN: Mailed survey. SETTING: Ontario hospitals. PARTICIPANTS: The CEOs of Ontario hospitals and family physicians who provide anesthetic services in Ontario hospitals. MAIN OUTCOME MEASURES: Demographics, practices, and opinions of FPAs and CEOs regarding family medicine anesthesia. RESULTS: Responses were received from 159 of 195 practising FPAs (82%). Of the 128 hospitals in Ontario that offered anesthesia services, 59% used at least one FPA; in 39% of these hospitals, all services were provided by FPAs. Both FPAs and CEOs thought that FPAs were competent to meet the anesthesia needs of small community hospitals. Most FPAs and CEOs supported certification and maintenance of competence programs coordinated by a national body, such as the College of Family Physicians of Canada. Both FPAs and CEOs thought there should be support for additional training programs in family medicine anesthesia. CONCLUSION: Small community hospitals rely completely on FPAs to provide essential anesthesia services. Additional training programs and a national structure to coordinate certification and maintenance of competence programs are important to maintain and enhance this essential service.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".