Implementing Psychological Interventions Through Nonspecialist Providers and Telemedicine in High-Income Countries: Qualitative Study from a Multistakeholder Perspective
Bibliographic record
Abstract
BACKGROUND: Task sharing has been used worldwide to improve access to mental health care, where nonspecialist providers-individuals with no formal training in mental health-have been trained to effectively treat perinatal depressive and anxiety symptoms. Little formative research has been conducted to examine relevant barriers and facilitators of nonspecialist providers and the use of telemedicine in treatment service delivery. OBJECTIVE: The primary objective of this study was to examine the main barriers and facilitators of nonspecialist provider-delivered psychological treatments for perinatal populations with common mental health disorders, such as depression and anxiety, from a multistakeholder perspective. METHODS: This study took place in Toronto, Canada. In total, 33 in-depth interviews were conducted with multiple stakeholder groups (women with lived experience and their significant others, as well as health and mental health professionals). Qualitative data were quantified to estimate commonly endorsed themes within and across stakeholder groups. RESULTS: Psychological treatments delivered by nonspecialist providers were considered acceptable by the vast majority of participants (30/33, 90%). Across all stakeholder groups, nurses (20/33, 61%) and midwives (14/33, 42%) were the most commonly endorsed cadre of nonspecialist providers. The majority of stakeholders (32/33, 97%) were amenable to nonspecialist providers delivering psychological treatment via telemedicine (27/33, 82%), although concerns were raised about the ability to establish a therapeutic alliance via telemedicine (16/33, 48%). Empathy was the most desired characteristic of a nonspecialist provider (61%). Patient and patient advocate stakeholders were more likely to emphasize stigma as an important barrier to accessing psychological treatments (7/12, 58%), compared to clinicians (2/9, 22%) and spouses (1/5, 20%). Clinician stakeholders were more likely to emphasize the importance of ensuring nonspecialist providers were trained to deliver psychological treatments (3/9, 33%), compared to other stakeholder groups. CONCLUSIONS: These results can inform the design, implementation, and integration of nonspecialist-delivered interventions via telemedicine for women with perinatal depressive and anxiety symptoms in high-income country contexts.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| 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".