School Psychology and COVID-19 in Canada: Turning crisis into opportunities
Bibliographic record
Abstract
The Coronavirus 2019 (COVID-19) caused a significant crisis for the educational system and its students worldwide. School psychologists were among the many professionals who adapted their work to meet the needs of schools and students during the pandemic. This study aimed to gain a deeper understanding of school psychologists’ experiences regarding the changes and adaptations in their practices during COVID-19 and evaluate their perceptions of the challenges and benefits associated with these changes. Another goal was to investigate their thoughts on what should be preserved, modified, or improved in the field. Twenty school psychologists working in schools across Canada were interviewed online using a modified version of the semi-structured interview developed by Reupert et al. (2022b). Interviews were transcribed and analyzed using reflexive thematic analysis. Six themes were identified, including (a) disruptions and challenges in services, (b) making it work - adapting one’s work, (c) coping with change - influences of work support & personal strategies, (d) effects of the COVID-19 pandemic on students, (e) school psychology work post-COVID-19, and (f) lessons learned and recommendations for future pandemics. This study aims to be of educational and practical significance to school psychologists. It is critical that we learn from this challenging period imposed by the COVID-19 crisis and identify what should be preserved or modified in Canadian school psychologists' practice post-pandemic.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.039 | 0.017 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| 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".