Knowledge, attitudes, and experiences in suicide assessment and management: a qualitative study among primary health care workers in southwestern Uganda
Bibliographic record
Abstract
BACKGROUND: Suicide is one of the leading causes of death globally, with over 75% of all suicides occurring in low-and middle-income Countries. Although 25% of people have contact with their health care workers before suicide attempts, most never receive proper suicide assessment and management. We explored primary care health workers' knowledge, attitudes, and experiences in evaluating and managing suicidality in structured primary healthcare services in Uganda. METHODS: This was a cross-sectional qualitative study among health workers in southwestern Uganda from purposively selected health facilities. A semi-structured interview guide was used, and data were analyzed using thematic analysis. RESULTS: The in-depth interviews were conducted with 18 individuals (i.e., five medical doctors, two clinical officers, two midwives, and nine nurses) from 12 health facilities in the five selected districts. Four themes emerged from the discussions: a) Knowledge and attitudes of primary healthcare workers in the assessment and management of suicidality, b) Experiences in the assessment and management of suicidality, c) challenges faced by primary healthcare workers while assessing and managing suicidality, and d) Recommendations for improving assessment and management of suicidality in PHC. Most participants were knowledgeable about suicide and the associated risk factors but reported challenges in assessing and managing individuals with suicide risk. The participants freely shared individual experiences and attitudes in the assessment and management of suicide. They also proposed possible ways to improve the evaluation and management of suicidality in PHC, such as setting up a system of managing suicidality, regularizing community sensitization, and training health workers. CONCLUSION: Suicidality is commonly encountered by primary health care workers in Uganda who struggle with its assessment and management. Improving the knowledge and attitudes of primary health care workers would be a big step towards ensuring equitable services.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".