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Record W2004263820 · doi:10.1080/13548500902866939

Gender differences in cardiac patients: A longitudinal investigation of exercise, autonomic anxiety, negative affect and depression

2009· article· en· W2004263820 on OpenAlexaff
Tiffany T. Hunt-Shanks, Christopher Blanchard, Robert D. Reid

Bibliographic record

VenuePsychology Health & Medicine · 2009
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsUniversity of OttawaDalhousie UniversityBrock University
Fundersnot available
KeywordsAnxietyAffect (linguistics)Depression (economics)PsychologyHospital Anxiety and Depression ScalePhysical exerciseClinical psychologyMedicineInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Female cardiac patients frequently experience greater anxiety and depression and engage in less exercise when compared with their male counterparts. This study considered whether exercise had similar effects on male and female cardiac patients' autonomic anxiety, negative affect and depression, and whether exercise behavior explained the gender difference in their affective functioning (e.g. autonomic anxiety, negative affect and depression). Eight hundred one participants completed the Hospital and Anxiety Depression Scale (HADS) and the leisure score index (LSI) of the Godin Leisure-Time Exercise Questionnaire at baseline, 6 months, 12 months, and 24 months. Female cardiac patients had greater autonomic anxiety, negative affect and depression and reduced exercise when compared with male cardiac patients at all time points. Although exercise was significantly related to affective outcomes at various time points for both men and women, gender did not moderate any of the exercise/affective relationships, and exercise did not mediate any of the gender/affective relationships. Further research is needed to clarify the complex relationships between gender, exercise, and the affective functioning of cardiac patients.

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)0.001
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 score0.046
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.045
GPT teacher head0.385
Teacher spread0.340 · 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

Citations31
Published2009
Admission routes1
Has abstractyes

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