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Record W4284893497 · doi:10.1177/00144029221112285

School-to-Work Transition of Youth with Learning Difficulties: The Role of Motivation and Autonomy Support

2022· article· en· W4284893497 on OpenAlexafffund
Pascale Dubois, Frédéric Guay, Marie‐Catherine St‐Pierre

Bibliographic record

VenueExceptional Children · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsCentre for Interdisciplinary Research in RehabilitationUniversité Laval
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyGeneralizability theoryAutonomySelf-determination theoryStructural equation modelingDevelopmental psychologyPopulationSocial psychologyWork motivationTransition (genetics)School-to-work transitionWork (physics)PedagogyVocational education

Abstract

fetched live from OpenAlex

School-to-work transition is a challenging period for youth with learning difficulties (LD). Based on self-determination theory (SDT), we tested the role of autonomy support and motivation in predicting transition status and well-being among this population. This prospective study included 218 students with LD in their last year of a work-study program. They were surveyed at the end of the school year and 1 year later. Two structural equation models were tested: one with the transition status as the outcome and one with well-being. Analyses revealed that autonomy support from fathers was positively associated with autonomous motivation in both models, as was autonomy support from friends in the transition status model. Autonomous motivation positively predicted both outcomes, while controlled motivation negatively predicted them. In sum, the psychological resources proposed by SDT seem to matter for youth with LD, thereby providing support for the generalizability of SDT.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.013
GPT teacher head0.246
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations14
Published2022
Admission routes2
Has abstractyes

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