Teletherapy in Training: A Trying and Transformative Experience
Bibliographic record
Abstract
Recent changes in federal and state laws, largely brought on because of the novel coronavirus, have underscored the need for revolutionizing the clinical training experience for Marriage and Family Therapy (MFT) programs and their implementation of telehealth services. This article addresses the current telehealth training in MFT programs, featuring the process of teletherapy training at Nova Southeastern University's (NSU) Department of Family, including adaptation to current policies and procedures. This study utilized semistructured interviews with eight graduate- and post-graduate–level MFT students at NSU who received telehealth training prior to participating in a teletherapy-based clinical practicum at NSU's Brief Therapy Institute. The emergent themes from the analysis included crisis management, flexibility, and self-care. Lastly, we discuss the implications and limitations of our study and suggest areas of future research. The information provides knowledge on necessary training topics and expands the literature on teletherapy.
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 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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.025 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".