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Record W2761529778

Addressing the needs of individuals with learning challenges in group CBT

2017· dissertation· en· W2761529778 on OpenAlexaboutno aff
Julie Gingras

Bibliographic record

VenueLu Zone Ul (Laurentian University) · 2017
Typedissertation
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsnot available
Fundersnot available
KeywordsGroup (periodic table)PsychologyData scienceComputer science
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.723
Threshold uncertainty score0.719

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.221
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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