Addressing the needs of individuals with learning challenges in group CBT
Bibliographic record
Abstract
This practicum essay is based on a 450 hour placement that was done within the Mood \nand Anxiety Program via Health Sciences North in Sudbury, Ontario. Here, I gained some \nclinical experience and a better understanding of the applications of group Cognitive-Behavioral \nTherapy approaches (CBT) for adults (16 years and older) who suffer from mood disorders. My \nobjectives consisted of first, acquiring clinical skills and proficiency in the delivery of group \nCognitive Behavioral Therapy. Second: to develop an understanding of approaches to social \nwork assessment and individual therapy within MAP. Third, to adapt group CBT material to \naddress the needs for people with learning challenges, this final objective represents my main \nchallenge and represents the bulk of this essay. This project outlines some of the specific learning \nchallenges found in populations with cognitive impairments focusing on those with Asperger’s, \nmild intellectual disabilities and learning disabilities. Basic adaptations and teaching strategies \nare discussed in hopes of increasing accessibility and creativity with the CBT approach. The \nneed for individual services is also recognized. I am proud to say that this experience has \nallowed me to grow as a competent professional in the social work field.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".