“Thinking Outside the Box”: A Snapshot of Child and Adolescent Therapists’ Early Experiences Adapting Services to the COVID-19 Context
Bibliographic record
Abstract
COVID-19 has had numerous impacts on the general population, including increased physical health risks, social isolation, and prolonged uncertainty (Gruber et al., 2020). Some have argued these impacts disproportionately affect younger populations (Powell et al., 2020). Within Canada, there has been a sharp uprise in children/youth accessing the Kids Help Phone over the past 18 months (Watson, 2020), underscoring the importance of continued access to mental health supports for this population. Child/youth practitioners have faced the arduous task of transitioning their services online, which may be uniquely challenging to accomplish with younger clients. This study employed a mixed-methods concurrent embedded design to explore the experiences of 36 child/youth therapists in Ontario as they have navigated this new professional landscape. An interpretative phenomenological analysis revealed both negative and positive impacts of COVID-19. Therapists reported feelings of reduced self-efficacy, difficulties engaging with and interpreting nonverbal communication using virtual mediums, technological challenges, and personal challenges relative to balancing work/home commitments as primary challenges. Younger clients appeared most significantly impacted due to developmental challenges inherent in online engagement. In response, therapists described creatively adapting their services, relying on team members or others for support and/or consultation, and various self-care strategies. Positively, many therapists felt youth were benefitting from the increased accessibility of virtual services. Despite the challenges identified, a repeated-measures MANOVA revealed no significant changes to therapist self-reported state empathy levels when reflecting on challenging clinical interactions prior to and since the onset of COVID-19. Implications for future research and clinical practice are discussed.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| 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".