The influence of a telehealth project on healthcare professional recruitment and retention in remote areas in Mali: A longitudinal study
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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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".