Rural Community Well-being: The Perspectives of Health Care Managers in Southwestern Manitoba, Canada
Bibliographic record
Abstract
Health care issues in rural areas are dominating the public policy agendas throughout the western world. The issues are many and varied. Canada is no different in this regard, as there are many rural areas with concerns about health care delivery in the face of declining populations. With 80 percent of Canadians living in urban, making the case for designating health care resources in areas with declining populations becomes more difficult. The purpose of this paper is to investigate how health care managers perceive rural community well-being, including indicators of well-being and determinants of well-being. The paper begins with an overview of the literature that places the context of this research in three areas. First, the literature on social capital is reviewed as in recent years both the social and medical sciences have been adopting it for analyzing community health. Second, the determinants of health literature is reviewed as the paper is concerned with the factors (determinants) that affect rural community well-being. Third, an overview of the definitions and models of health and well-being is provided to establish the context for measuring rural community well-being. A model is then applied through a survey of the twenty health care managers responsible for the operations of the 44 health care and seniors’ centres located within the Assiniboine Regional Health Authority in southwestern Manitoba. The model also proves useful in identifying determinants of health as well as assessing the role of social capacity in fostering rural community well-being.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.034 | 0.006 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| 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".