Explanatory Models for Mental Distress Among University Students in Ethiopia: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Socio-culturally determined processes account for how individuals give meanings to health, illness, causal attributions, expectations from treatment, and related outcomes. There is limited evidence of explanatory models for mental distress among higher education institutions in Ethiopia. The objective of this study was to explore the explanatory models for mental distress among Wolaita Sodo University. METHODS: The current study used a phenomenological research approach, and we collected data from 21 students. The participants were purposively recruited based on eligibility criteria. Semi-structured interviews were conducted from December 2017 to January 2018 using the Short Explanatory Models Interview. The interviews were audio-recorded, transcribed into the Amharic language and translated into English. Data were analyzed using framework analysis with the assistance of open code software 4.02. RESULTS: Most students experienced symptoms of being anxious, fatigue, headaches and feelings of hopelessness. They labeled these symptoms like anxiety or stress. The most commonly reported causal explanations were psychosocial factors. Students perceived that their anxiety or stress was severe that mainly affected their mind, which in turn impacted their interactions with others, academic result, emotions and motivation to study. Almost all the students received care from informal sources, although they wanted to receive care from mental health professionals. They managed their mental distress using positive as well as negative coping strategies. CONCLUSION: The policy implication of our findings is that mental health interventions in higher education institutions in Ethiopia should take into account the explanatory models of students' psychological distress.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".