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Record W4282837251 · doi:10.1177/10870547221105061

Students’ Inattention Symptoms and Psychological Need Satisfaction During the Secondary School Transition: The Protective Role of Teachers’ Involvement

2022· article· en· W4282837251 on OpenAlexafffund
Stéphane Duchesne, André Plamondon, Catherine F. Ratelle

Bibliographic record

VenueJournal of Attention Disorders · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Substance Use and School Attendance
Canadian institutionsUniversité Laval
FundersSocial Sciences and Humanities Research Council of CanadaFonds de Recherche du Québec-Société et Culture
KeywordsPsychologyAnxietyCompetence (human resources)AutonomyAggressionClinical psychologyAssociation (psychology)Developmental psychologySocial psychologyPsychiatryPsychotherapist

Abstract

fetched live from OpenAlex

OBJECTIVE: This study examined the prospective relationship from student inattention symptoms to changes in their psychological need satisfaction (PNS) during their transition to secondary school. In doing so, it has explored whether this temporal association was moderated by teachers' involvement (TI). METHOD: = 11.82) followed in Grade 6 and Secondary 1 was selected from a stratified random list. RESULTS: Inattention symptoms predicted a decrease in autonomy and competence need satisfaction, after adjusting for gender, anxiety, aggression, and PNS at baseline. In addition, TI in Secondary 1 attenuated the association between inattention and autonomy need satisfaction decline. TI also predicted a smaller decrease in competence need satisfaction, over and above the contribution of inattention. CONCLUSIONS: Results support the importance of TI in PNS of students who are struggling with inattention throughout a critical transition. Implications for educational practices and research are discussed.

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)0.003
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.282
Teacher spread0.270 · 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

Citations6
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Attention DisordersSame topicYouth Substance Use and School AttendanceFrench-language works237,207