Factors Influencing Neurosurgeons’ Decision to Retain in a Work Location: A Qualitative Study
Bibliographic record
Abstract
INTRODUCTION: Physician retention is a serious concern to have an effective and efficient health system; the key challenge is how best to encourage and retain health providers in their work location. There have been considerable studies on factors influencing physicians' retention but little research has been conducted in Iran. This study aims to determine the affecting factors from neurosurgeons' viewpoint to support policy makers in proposing a sort of evidence based retention strategies. METHODS: We conducted semi structured interviews with 17 neurosurgeons working in 9 provinces of Iran between September and November 2014. We included physicians remaining to work in a particular community for at least 3 years and asked them about the factors influenced their decision to retain in a work place. Data were thematically analyzed using "framework approach" for qualitative research. RESULTS: Satisfaction with monetary incentives, availability of adequate clinical infrastructure in a community and appropriate working condition were most commonly cited factors mentioned by all participants as key reasons for retention. Furthermore elements which contributed to the quality of living condition, personal background and incentives, family convenience were emphasized by majority of them. A small number of participants mentioned opportunity for continuing learning and updating knowledge as well as supportive organizational policies as important motivators in a workplace. CONCLUSION: Ministry of Health and Medical Education (MOHME) should consider a multifaceted and holistic approach to improve neurosurgeons' retention in their work location. Our findings suggests a combination of financial remuneration, establishment of adequate hospitals and clinical facilities, collaborative working environment with reasonable workload, proper living condition, family support and facilities for professional development to be employed as an effective strategy for promoting physicians' 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".