The Impact of Telehealth Integration on Rural Health care Professional Retention and Job Satisfaction
Bibliographic record
Abstract
PURPOSE: Rural communities face significant challenges in recruiting and retaining health care professionals (HCP) due to high workloads, lack of financial incentives, and demanding occupational requirements. Recent research shows that telehealth has the potential to enhance holistic care quality and reduce barriers to patient care; however, its potential to impact rural HCP retention remains unexplored. The purpose of this review was to assess the relationship between telehealth and rural HCP professional satisfaction and retention. METHODS: A systematic review was conducted following the Cochrane guidelines. MEDLINE ALL and Embase were searched from January 2014 to February 2025. Studies assessing the use of telehealth in rural health care settings and reporting on retention rates or professional satisfaction were included. Due to significant expected heterogeneity, studies were analyzed narratively. FINDINGS: The search identified 1,678 unique citations, with 38 studies proceeding to full-text review. Four studies were included in the final analysis: two mixed-methods studies and two qualitative studies. Each study assessed a different telehealth intervention, including tele-emergency, wound care, and general practice. Job satisfaction was reported in three studies, with personal fulfillment and professional development commonly reported. Retention was reported in two studies, with minimal actual impacts on retention but with HCPs perceiving considerable potential. Importantly, there were no quantitative measures of job satisfaction or retention reported. CONCLUSIONS: Despite telehealth being lauded as a way to improve retention and professional satisfaction in rural health care settings, there are very few studies assessing these outcomes. Though telehealth qualitatively improved professional satisfaction, there were minimal impacts on retention.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".