Advancing telemedicine and task-sharing to improve access to psychotherapy for perinatal populations
Bibliographic record
Abstract
The Scaling Up Maternal Mental healthcare by Increasing access to Treatment (SUMMIT) trial (ClinicalTrials.gov: NCT04153864) examined two solutions to improving access to psychotherapy for perinatal populations: telemedicine and task-sharing with non-specialist providers (individuals without prior specialized training or experience in delivering mental healthcare). The SUMMIT trial showed that telemedicine and non-specialist-delivered psychotherapy were non-inferior to treatment delivered in-person or by a specialist mental health provider. Our aim was to conduct an implementation assessment of task-sharing and telemedicine to inform best practices when providing access to psychotherapy treatment for perinatal patients in real world healthcare settings. In this current study, we examined barriers and facilitators of task-sharing and telemedicine-delivered psychotherapy from a multistakeholder perspective (N = 105). We interviewed perinatal participants (n = 70) who received psychotherapy and specialist or non-specialist providers (n = 35) who delivered psychotherapy in the SUMMIT trial. We conducted an inductive thematic analysis. Our results show many facilitators of telemedicine across all stakeholder groups, including alleviating childcare needs through convenience, flexibly, and increased accessibility. Although perinatal participants and providers express that there are some benefits of in-person delivery (e.g., seeing physical cues and minimizing privacy concerns), we find that there are more barriers than facilitators of in-person psychotherapy. Regarding task-sharing, perinatal participants who received treatment from non-specialist and specialist providers report the same facilitators at similar rates, including capacity for active listening and empathy. Our implementation assessment shows that telemedicine and task-sharing are acceptable, feasible, and patient-centred solutions to improve access to evidence-based psychotherapies across healthcare settings. The Scaling Up Maternal Mental healthcare by Increasing access to Ṯreatment (SUMMIT) trial tested two approaches to improve access to mental healthcare for pregnant or postpartum patients with depression or anxiety. This included (1) task-sharing with non-specialists (e.g., nurses and midwives) delivering a brief talk therapy via (2) telemedicine (online delivery). We interviewed 70 pregnant or postpartum participants and 35 providers from the study to understand the benefits and challenges of these approaches. Many people found telemedicine helpful in reducing common challenges, such as the need for childcare and travel. While some preferred in-person visits to pick up on body language, more challenges were reported with in-person care. Participants who received therapy from a non-specialist had similar positive experiences to those who received therapy from a specialist (e.g. a psychologist, psychiatrist or social worker). Overall, the study shows that non-specialist and telemedicine-delivered therapy are practical, patient-centred ways to improve mental healthcare during and after pregnancy. Andrejek et al. evaluate the barriers and facilitators of leveraging telemedicine and task-sharing to improve access to psychotherapy for perinatal patients with symptoms of depression and anxiety in the SUMMIT trial. The qualitative implementation assessment demonstrates that both solutions are feasible, patient-centered approaches.
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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 | 0.023 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".