Clinicians’ Decision-Making Regarding Telehealth Services: Focus Group Study in Pediatric Allied Health
Bibliographic record
Abstract
BACKGROUND: Many allied health services now provide both telehealth and in-person services following a rapid integration of telehealth as a response to the COVID-19 pandemic. However, little is known about how decisions are made about which clinical appointments to provide via telehealth versus in person. OBJECTIVE: The aim of this study is to explore clinicians' decision-making when contemplating telehealth for their clients, including the factors they consider and how they weigh up these different factors, and the clinicians' perceptions of telehealth utility beyond COVID-19 lockdowns. METHODS: We used reflexive thematic analysis with data collected from focus groups with 16 pediatric community-based allied health clinicians from the disciplines of speech-language pathology, occupational therapy, social work, psychology, and counseling. RESULTS: The findings indicated that decision-making was complex with interactions across 4 broad categories: technology, clients and families, clinical services, and clinicians. Three themes described their perceptions of telehealth use beyond COVID-19 lockdowns: "flexible telehealth use," "telehealth can be superior to in-person therapy," and "fear that in-person services may be replaced." CONCLUSIONS: The findings highlight the complexity of decision-making in a community-allied health setting and the challenges experienced by clinicians when reconciling empirical evidence with their own clinical experience.
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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 | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".