The influence of a telehealth project on healthcare professional recruitment and retention in remote areas in Mali: A longitudinal study
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVES: The telehealth project EQUI-ResHuS (in French, Les TIC pour un accès Équitable aux Ressources Humaines en Santé) aimed to contribute to more equitable access to care and support practice in remote regions in Mali. This study explored the evolution of perceptions concerning telehealth among healthcare professionals in the four district health centres that participated in the EQUI-ResHus project and identified variables influencing their perceptions of telehealth impact on recruitment and retention of health professionals. METHODS: One year after a first survey (T1), a second data collection (T2) was carried out among healthcare professionals using a 91-item questionnaire. Questions assessing telehealth use and perceptions and perceived impact on recruitment and retention of healthcare professionals were rated on a 5-point Likert scale. A total of 10 independent variables were considered for the analyses. A Wilcoxon signed-rank test was performed to detect differences between T1 and T2, and a bivariate linear regression model for repeated measures was carried out to assess the impact of independent variables on dependent variables. RESULTS: There were no noticeable changes in perceptions related to telehealth influence on recruitment and retention. Only access to information and communication technology significantly differed between T1 and T2 according the Wilcoxon rank test (p = 0.001). Perceived influence of telehealth on recruitment and retention was mostly explained by attitude towards telehealth, perceived effect on recruitment and retention and barriers to recruitment and retention. CONCLUSION: Based on our results, telehealth was perceived as having a positive influence but mostly indirect influence on healthcare professional recruitment and retention. Also, there were no major changes after 1 year of telehealth use.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it