Videoconferencing at home for psychotherapy in the postpartum period: Identifying drivers of successful engagement and important therapeutic conditions for meaningful use
Bibliographic record
Abstract
Abstract Purpose Virtual care has almost become the norm since COVID‐19 mandated social distancing. Prior, the use of personal videoconferencing was being explored as an appealing option to overcome barriers to care, but little was studied about how we should do it and for whom. This study aimed to answer these questions for women receiving psychotherapy in the postpartum period when there are significant barriers to attending office‐based care. Methods Twelve postpartum women who had the option to attend their psychotherapy sessions for the treatment of mood and/or anxiety symptoms via videoconferencing over a 3‐month time period were interviewed about their experience. The three therapists providing care were also interviewed early in adoption of the virtual treatment and at the end of the study. Thematic analysis was conducted to identify themes for the initial and ongoing engagement with videoconferencing, which were triangulated with therapist input. Findings Major themes which emerged related to (a) initial willingness to engage with videoconferencing, (b) technological compatibility, and (c) a good patient fit, with positive and negative influencers for each. Therapeutic considerations were identified, including (a) an initial in‐person meeting when possible, (b) matching the therapy format to the clinical situation, (c) attention to the home environment, and (d) a clear therapy frame. Conclusions Therapists should consider that videoconferencing might not be appropriate for every patient; but in the right context and with appropriate therapeutic considerations, offering this treatment format may actually facilitate an individual's recovery. These findings can help to inform the future delivery of videoconferencing psychotherapy.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".