Supporting Health Professionals Through Information and Communication Technologies: A Systematic Review of the Effects of Information and Communication Technologies on Recruitment and Retention
Bibliographic record
Abstract
UNLABELLED: Healthcare personnel shortage is a growing concern in many countries, especially in remote areas, where it has major consequences on the accessibility of health services. Information and communication technologies (ICTs) have often been proposed as having positive effects on certain dimensions of the recruitment and retention of professionals working in the healthcare sector. OBJECTIVE: This study aims to explore the impact of interventions using ICTs on recruitment and retention of healthcare professionals. MATERIALS AND METHODS: A systematic review of the literature was conducted, including the following steps: exploring scientific and gray literature through established criteria and data extraction of relevant information by two independent reviewers. RESULTS: Of the 2,225 screened studies, 13 were included. Nine studies showed a positive, often indirect, influence that ICTs may have on recruitment and retention. CONCLUSIONS: Despite the conclusions of 9 of 13 studies reporting a possible positive influence of ICTs on the recruitment and retention of healthcare professionals, these results highlight the need of a deeper reflection on that topic. Therefore, more research is needed.
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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 | 0.019 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".