A Nonprofit's Transition to Teletherapy Due to the COVID-19 Crisis: Learning How to Adapt
Bibliographic record
Abstract
Due to the state of the COVID-19 in Florida, a community-based agency serving children and families had to transition abruptly to teletherapy. This agency adapted to pandemic-related challenges by transitioning from in-person to virtual therapy, which played a key role in safely serving community members. This article explores the unique benefits and difficulties of the transition to teletherapy under those circumstances. The agency clinical staff utilized their brief therapy skills and strengths-oriented perspective to aid in this abrupt transition, as illustrated by a case study. Ultimately, the agency's transition was a successful one as evidenced by a survey of both agency clinicians and clients, and by uninterrupted services at the same volume of cases and level of care. Suggestions are made for other providers seeking to cope with similar transitions.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| 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".