Utilization of primary health care nurse practitioners in rural northern communities to alleviate the physician shortages and retention issues / by Susan Fairservice.
Bibliographic record
Abstract
The purpose of this research project is to critically examine primary health care services \nand analyze alternative solutions to the physician recruitment and retention issue in rural, \nnorthern, and remote communities in Ontario. Primary health care nurse practitioners, (PHCNPs) \nare a safe, cost-effective solution to alleviate this dilemma in these communities. \nThe needs assessment, conceptual framework and literature review demonstrated that \npopulations living in rural, northern and remote communities have decreased access to Health \nCare Providers, show that PHCNPs are a cost-effective and safe alternative to physicians, and \ndemonstrate that recruiting nurses from rural and remote communities to become PHCNPs is \nbeneficial to the communities. The Population Health Model validated that the health care needs \nof individuals in rural, northern and remote communities are not being met \nTo alleviate the physician shortage and retention issue in Ontario and particularly rural, \nnorthern and remote communities in Ontario the Ministry of Health and Long Term Care \n(MOHLTC) could: \nProvide funding to Registered Nurses in northern, rural and remote \ncommunities to become PHCNPs. \nIncrease the number of PHCNP run clinics. \nContinue to support and increase the number of Family Health Teams in \nOntario.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".