Videoconferencing Psychotherapy in the Public Sector: Synthesis and Model for Implementation
Bibliographic record
Abstract
BACKGROUND: Videoconferencing psychotherapy (VCP) is a growing practice among mental health professionals. Early adopters have predominantly been in private practice settings, and more recent adoption has occurred in larger organizations, such as the military. The implementation of VCP into larger health service providers in the public sector is an important step in reaching and helping vulnerable and at-risk individuals; however, several additional implementation challenges exist for public sector organizations. OBJECTIVE: The aim of this study was to offer an implementation model for effectively introducing VCP into public sector organizations. This model will also provide practical guidelines for planning and executing an embedded service trial to assess the effectiveness of the VCP modality once implemented. METHODS: An iterative search strategy was employed, drawing on multiple fields of research across mental health, information technology, and organizational psychology. Previous VCP implementation papers were considered in detail to provide a synthesis of the barriers, facilitators, and lessons learned from the implementation attempts in the military and other public sector settings. RESULTS: A model was formulated, which draws on change management for technology integration and considers the specific needs for VCP integration in larger organizations. A total of 6 phases were formulated and were further broken down into practical and measurable steps. The model explicitly considers the barriers often encountered in large organizational settings and suggests steps to increase facilitating factors. CONCLUSIONS: Although the model proposed is time and resource intensive, it draws on a comprehensive understanding of larger organizational needs and the unique challenge that the introduction of VCP presents to such organizations.
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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 | 0.033 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".