Psychiatry as a specialization: influential factors and gender differences among medical students in a low- to middle-income country
Bibliographic record
Abstract
OBJECTIVES: To assess the perception of Ghanaian medical students about factors influencing their career interest in psychiatry and to explore gender differences in these perceptions. METHODS: This is a cross-sectional quantitative survey of 5th and 6th year medical students in four public medical schools in Ghana. Data were analyzed with descriptive and inferential statistics using SPSS version 20. RESULTS: Responses were obtained from 545 medical students (response rate of 52%). Significantly, more male medical students expressed that stigma is an important consideration for them to choose or not to choose a career in psychiatry compared to their female counterparts (42.7% v. 29.7%, respectively). Over two-thirds of the medical students perceived that psychiatrists were at risk of being attacked by their patients, with just a little over a third expressing that risk was an important consideration for them to choose a career in psychiatry. There were no gender differences regarding perceptions about risk. Around 3 to 4 out of 10 medical students will consider careers in psychiatry if offered various incentives with no gender differences in responses provided. CONCLUSION: Our study presents important and novel findings in the Ghanaian context, which can assist health policy planners and medical training institutions in Ghana to formulate policies and programs that will increase the number of psychiatry residents and thereby increase the psychiatrist-to-patient ratio in Ghana.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".