Exploring the effects of telehealth on medical human resources supply: a qualitative case study in remote regions
Bibliographic record
Abstract
Abstract Background The availability of medical human resource supply is a growing concern for rural and remote communities in many countries. In the last decade, various telehealth experiences in Canada have highlighted the potential impact of this technology on professional practice. The purpose of this study was to explore physicians' and managers' perceptions regarding the potential of telehealth to support recruitment and retention of physicians in remote and rural regions. Methods A case study in Eastern Quebec was performed to explore this complex phenomenon. The analytical framework was based on two literature reviews and a Delphi study. Data were collected from semi-structured interviews with 41 physicians and 22 managers. Transcripts were produced and interview content was coded independently by two judges and validated by an expert panel. Results Interviews have highlighted the potential impact of telehealth on several factors influencing the recruitment and retention of physicians in rural and remote regions. The potential effects of telehealth on physicians' choice of practice location could be seen at the professional, organizational, educational and individual levels. For instance, telehealth could improve work satisfaction by allowing a regional on-call duty system and a better follow-up of patients. However, there are also certain limits related to telehealth, such as the fear that it would eventually replace all continuing medical education activities and onsite specialists in remoteregions. Conclusion Telehealth is likely to have an impact on several factors related to medical workforce supply in remote and rural regions. However, the expected benefits will materialize if and only if this technology is properly integrated into organizations as a support to professional practice.
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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 | 0.001 | 0.003 |
| 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) | 0.117 | 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".