Facilitators in treatment pathways for depression or anxiety among adults in Nepal: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Depression and anxiety are prevalent mental health issues globally, yet many individuals in low- and middle-income countries lack access to treatment. Limited research exists on mental health service utilization in these regions. Understanding the factors that affect access to care and treatment pathways can improve mental health services. This study examines the factors that facilitate the initiation and continuation of treatment for depression or anxiety in Nepal. METHODS: The study was conducted in three districts in Nepal: Jhapa, Chitwan, and Kailali districts, representing the eastern, central, and far-western regions. The participants were adults receiving treatment for depression or anxiety from various healthcare providers. A total of 24 participants were purposively recruited, including 13 with symptoms of depression, 9 with symptoms of anxiety, and 2 with both conditions. We utilized the McGill Illness Narrative Interview, a semi-structured protocol commonly used in mental health research, to collect detailed narratives on symptom experiences, illness accounts, and help-seeking behaviors. Data analysis was performed using a framework and thematic analysis approach with NVIVO software. RESULTS: Treatment pathways for depression and anxiety in Nepal are complex, involving multiple service providers and recurrent treatment from the same providers. Out of a total of 137 sessions across 24 patients, the majority of sessions were with traditional faith healers (27.7%), followed by private hospitals (19.7%), primary healthcare facilities (16.1%), government hospitals (13.1%), neighboring countries (11.7%), and private clinics (8.0%). Traditional healers were the most popular choice for initial visits, followed by private clinics and government hospitals. Factors such as service quality, provider behavior, availability of trained providers, appointment process, confidentiality, and types of services offered influenced care-seeking decisions. Support from family or friends, awareness of mental health issues, and recommendations from trusted individuals also played a significant role. CONCLUSION: Treatment pathways for depression and anxiety disorders are complex, often involving multiple sessions with various service providers and a combination of services. It is crucial to improve healthcare providers' behavior, appointment scheduling, and consultation quality to encourage individuals to seek care. Raising awareness about mental health conditions and available services through different channels and training traditional healers in mental health could help enhance access to care.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| 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".