SELF-REGULATION AND COGNITIVE BEHAVIOUR THERAPY IN CANADIAN SCHOOLS
Bibliographic record
Abstract
Student well-being and behaviour is assessed daily by classroom teachers at school and teachers are also asked occasionally to complete evidence-based psychology reports regarding a particular student in their class to inform clinical practice and help with diagnoses. This critical qualitative study considers the role of surveillance in schools as a tool to keep students safe and ensure well-being. Data from a two-year qualitative study provides insight from teachers, administration and IT staff regarding the use of surveillance in schools and considers ways that data can be used to assist in cognitive behaviour therapy, as well as discussing the protection of data for vulnerable and marginalized students from a FOIPPA compliance perspective. Discussions emerge as to the potential use of data tracking and data collection for staff to identify and conduct cognitive behaviour therapy (CBT) in schools. The ability to use digital education records combined with advancements in technology might enable the same deep learning in education as in medicine in the areas self-regulation. Results from the study indicate. Information Technology (IT) staff struggle with their application of privacy matters and may not be using data tracking as a means to develop and document well-being for students and staff.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".