Risk factors of professional burnout for nurses, health technicians and midwives at the beni mellal regional hospital, Morocco
Bibliographic record
Abstract
INTRODUCTION: Burnout is a topical issue, which concerns all fields and more particularly our health field. OBJECTIVES: Our descriptive study aims to evaluate the prevalence of burnout and describe its risk factors among nurses, health technicians and midwives in the regional hospital of Beni Mellal. It is being carried out from February to June 2022, with 113 participants. METHODS: Given the nature of our research, the data collection tool consists of two questionnaires, the first to study personal, professional data and risk factors for burnout, and the second to assess burnout among our participants, based on the MBI in its French version. RESULTS: Our study showed that burnout affected more than three quarters of our sample, 59.3% of them had high emotional exhaustion, 26.5% had high depersonalization and 41.6% had low personal accomplishment. The occurrence of this syndrome was the result of several risk factors, the most frequent being: stress related to the Covid-19 pandemic, poor organization and management of services, insufficient means and personnel, lack of recognition and motivation, unsatisfactory salary/effort, degraded interpersonal relations and confrontation with suffering. CONCLUSIONS: In conclusion, burnout is a palpable reality among nurses, health technicians and midwives.Our alarming results must lead to the implementation of preventive actions while insisting on the organization of work and the valorization of the Moroccan caregiver. DISCLOSURE OF INTEREST: None Declared
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".