Defining the Role of the Online Therapeutic Facilitator: Principles and Guidelines Developed for Couplelinks, an Online Support Program for Couples Affected by Breast Cancer
Bibliographic record
Abstract
Development of psychological interventions delivered via the Internet is a rapidly growing field with the potential to make vital services more accessible. However, there is a corresponding need for careful examination of factors that contribute to effectiveness of Internet-delivered interventions, especially given the observed high dropout rates relative to traditional in-person (IP) interventions. Research has found that the involvement of an online therapist in a Web-based intervention reduces treatment dropout. However, the role of such online therapists is seldom well articulated and varies considerably across programs making it difficult to discern processes that are important for online therapist involvement.In this paper, we introduce the concept of "therapeutic facilitation" to describe the role of the online therapist that was developed and further refined in the context of a Web-based, asynchronous psychosocial intervention for couples affected by breast cancer called Couplelinks. Couplelinks is structured into 6 dyadic learning modules designed to be completed on a weekly basis in consultation with a facilitator through regular, asynchronous, online text-based communication.Principles of therapeutic facilitation derived from a combination of theory underlying the intervention and pilot-testing of the first iteration of the program are described. Case examples to illustrate these principles as well as commonly encountered challenges to online facilitation are presented. Guidelines and principles for therapeutic facilitation hold relevance for professionally delivered online programs more broadly, beyond interventions for couples and cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".