Recruitment and retention of remote and rural health care staff: making it work
Bibliographic record
Abstract
Recruiting and retaining health care staff and other skilled employees is a common problem in remote and rural communities. There is no single solution to inadequate workforce supply; a whole system approach should be taken to address this complex issue. Multiple initiatives that bring incremental improvements are most likely to ensure an overall impact. Making it Work (MIW) will implement five recruitment and retention case studies, at scale, across Northern Europe and Canada, using a business model tailored to local and regional needs. This will develop previous work which sought to understand recruitment and retention issues, and which proposed a series of solutions, and a business model. MIW will apply a community focussed lens, a detailed planning process, system redesign, and will conduct a structured evaluation highlighting costs and benefits of intervention. A flexible policy framework and a practical online toolkit will be developed to allow knowledge transfer of key factors of success. This workshop will describe the results of previous work in more detail, and will outline the approach to MIW. Participants will be encouraged to share their experiences, and to consider innovative solutions to this complex issue.This abstract was presented at the Innovative Solutions in Remote Healthcare - 'Rethinking Remote' conference, 23-24 May 2016, Inverness, Scotland.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".