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Record W4200514491 · doi:10.5708/ejmh.16.2021.2.4

Resilience intervention to Strengthen Self-Regulation in adolescent students with hearing loss

2021· article· en· W4200514491 on OpenAlexfundno aff
Narges Adibsereshki, Nikta Hatamizadeh, Anoshirvan Kazemnejad, Firoozeh Sajedi

Bibliographic record

VenueEuropean Journal of Mental Health · 2021
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsnot available
FundersConcordia UniversityJohns Hopkins University
KeywordsHearing lossPsychological resilienceIntervention (counseling)PsychologyTest (biology)Clinical psychologyResilience (materials science)MainstreamDevelopmental psychologyAudiologyMedicinePsychiatrySocial psychology

Abstract

fetched live from OpenAlex

Background: The importance of strong self-regulation (SR) abilities for academic and social success is known, yet relatively few studies examine students’ SR and how it can be promoted especially in adolescents with special needs, such as those students with hearing loss. The purpose of this study was to determine whether a resilience intervention program enhances SR in adolescents with hearing loss. Methods: This study was experimental with a pre-test, post-test, follow up and control group design. Participants included 122 students with hearing loss in mainstream settings randomly assigned to intervention and control groups (61 students in each group). The interventional group had training for six weeks (two times per week for 75 min). The Adolescent Self-Regulatory Inventory was used to measure the self-regulation of students. Results: The results indicated a significant difference between the control and interventional groups in SR, short SR, and Long SR after the intervention, at both the 6-week and 14-week measurements (p < 0.001). Conclusion: This study’s findings indicate that implementing resilience intervention programs can promote the self-regulation skills in adolescent students with hearing loss.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.062
GPT teacher head0.406
Teacher spread0.344 · 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 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

Citations1
Published2021
Admission routes1
Has abstractyes

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