Factors influencing the career choice and retention of community mental health workers in Ghana
Bibliographic record
Abstract
BACKGROUND: Whilst there have been several studies exploring retention in health workers, little is known about health workers engaged in the provision of mental health services and the factors that affect their recruitment and retention. AIMS: The objective of this research was to examine the views of stakeholders about the factors which influence career choices and retention of community mental health workers (CMHWs) in Ghana. METHODS: We administered three separate, self-administered, semi-structured questionnaires to 11 psychiatrists, 29 health policy directors and 164 CMHWs across Ghana, including 71 (43.3%) community psychiatric nurses (CPNs), 19 (11.6%) clinical psychiatric officers (CPOs) and 74 (45.1%) community mental health officers (CMHOs). RESULTS: Overall, 34 (20.7%) of all CMHWs chose to work in mental health because of the job prospects in mental healthcare. Overall, 12 (16.2%) CMHOs, 1 (5.3%) CPO and 20 (28.2%) CPNs reported they had considered leaving the mental health profession because of the stigma, with 4 (36.4%) psychiatrists and 12 (41.4%) health policy coordinators also reporting that they knew some CMHWs who had considered leaving the mental health profession because of stigma. Similarly, 16 (21.6%) CMHOs, 4 (22.1%) CPOs and 38 (53.5%) CPNs said they had considered leaving the mental health profession because of concerns about risk. Furthermore, 6 (54.5%) psychiatrists and 3 (10.3%) health policy directors said they knew some CMHWs who had considered leaving the mental health profession because of concerns about risk. Overall, 61 (37.2%) of CMHWs reported that they have considered leaving the mental health profession for other reasons other than stigma and risk including the following: the lack of support, respect and recognition from healthcare managers, lack of opportunities for professional development and poor conditions of service including low salaries, lack of office and personal accommodation and lack of risk allowance and transportation as well as poor inter-professional relationships. CONCLUSIONS: Several factors affect the recruitment and retention of CMHWs in Ghana, including the prospects of easy employment, stigma, risk, lack of opportunities for career progression and low salaries.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".