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Record W2103588397 · doi:10.1093/jpepsy/jsp061

Experimentally-Induced Learned Helplessness in Adolescents with Type 1 Diabetes

2009· article· en· W2103588397 on OpenAlexafffund
E. McLaughlin, Marie-josée Lefaivre, Elizabeth Cummings

Bibliographic record

VenueJournal of Pediatric Psychology · 2009
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsIzaak Walton Killam Health CentreDalhousie University
FundersCanadian Diabetes Association
KeywordsLearned helplessnessAttributionAnagramAnagramsPsychologyType 2 diabetesTask (project management)Diabetes mellitusDevelopmental psychologyType 1 diabetesClinical psychologySocial psychologyMedicineEndocrinology

Abstract

fetched live from OpenAlex

OBJECTIVES: To determine whether adolescents with type 1 diabetes are more at risk for learned helplessness than their healthy peers. METHODS: Twenty-three adolescents with diabetes and 25 controls completed a solvable or unsolvable concept formation task. All completed pre- and post-task performance and attribution ratings, and later completed an anagram-solving task to determine if perceived helplessness on the first task would negatively impact performance on the second. RESULTS: Participants in the unsolvable condition solved fewer anagrams; those with diabetes did not show weaker performance than controls. Participants in the solvable condition (diabetes and controls) showed an increase in internal attributions from before the concept formation task to after. In the unsolvable condition, only participants with diabetes made more external attributions for their failure. CONCLUSIONS: Contrary to the only other controlled study to use this paradigm in youth with chronic illness, adolescents with diabetes were not more susceptible to learned helplessness.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0000.001
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 head0.045
GPT teacher head0.377
Teacher spread0.332 · 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 designBench or experimental
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

Citations13
Published2009
Admission routes2
Has abstractyes

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