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Record W2332658173 · doi:10.1177/0008417415627665

Self-regulation workshop and Occupational Performance Coaching with teachers: A pilot study

2016· article· en· W2332658173 on OpenAlexvenueaboutno aff
Caroline Hui, Laurie Snider, Mélanie Couture

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2016
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
Fundersnot available
KeywordsGoal Attainment ScalingCoachingPsychologyOccupational therapyMedical educationClassroom managementApplied psychologyPerceptionScale (ratio)Intervention (counseling)Time managementMultiple baseline designMedicinePedagogyComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Teachers' occupational role and performance can be undermined when working with students with disruptive classroom behaviours. PURPOSE: This pilot study aimed to explore the impact of school-based occupational therapy intervention on teachers' classroom management self-efficacy and perceived performance/satisfaction in their management of students with disruptive behaviours. METHOD: This pilot study used a multiple-case replication study design. A cohort of regular classroom elementary school teachers (n = 11) participated in a 1-day workshop on sensorimotor strategies for supporting student self-regulation followed by eight individual sessions of Occupational Performance Coaching (OPC). Measurement tools were the Canadian Occupational Performance Measure, Goal Attainment Scaling (GAS), and Teachers' Self-Efficacy Scale-Classroom Management. FINDINGS: Improvement in teachers' perception of performance, satisfaction, and classroom management was seen. GAS showed clinically significant improvement. Improvements were sustained at 7 weeks follow-up. IMPLICATIONS: Preliminary results support the use of sensorimotor education combined with OPC to enable teachers' occupational performance.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.272
GPT teacher head0.470
Teacher spread0.197 · 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.

Study designObservational
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

Citations52
Published2016
Admission routes2
Has abstractyes

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