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Record W2897084388 · doi:10.1177/1557988318803744

The Role of Positive Emotion and Ego-Resilience in Determining Men’s Physical Activity Following a Workplace Health Intervention

2018· article· en· W2897084388 on OpenAlexafffund
Cherisse L. Seaton, Joan L. Bottorff, Margaret Jones-Bricker, Sonia Lamont

Bibliographic record

VenueAmerican Journal of Men s Health · 2018
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsBC Cancer AgencyCanadian Cancer SocietyUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersCanadian Cancer Society Research Institute
KeywordsId, ego and super-egoPsychologyIntervention (counseling)Resilience (materials science)Psychological resilienceClinical psychologySocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

Men are a hard-to-reach group in the promotion of modifiable behaviors such as physical activity. Examining the individual differences among men that might predict positive behavior changes could support customization of health promotion programs. This study examined the role of emotional outlook, positive emotion, and ego-resilience in determining men’s physical activity and health-related quality of life following implementation of a gender-sensitive workplace health intervention. Using a pre–post within-subjects design, computer-assisted telephone interviewing (CATI) was used to collect measures of emotion and ego-resilience along with physical activity and health-related quality of life (using the 12-item short form [SF-12]) at baseline ( n = 139) and after 6 months ( n = 80) from adult men ( M age = 43.7, SD = 12.5). Baseline emotional outlook and ego-resilience were both positively related to increased physical activity at follow-up among men. Emotional outlook and positive emotion were positively related to ego-resilience, and ego-resilience mediated the relationship between these and the physical component of health-related quality of life. Workplace health interventions that incorporate the promotion of personal resources hold potential for greater impacts.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.951
Threshold uncertainty score0.517

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 head0.011
GPT teacher head0.401
Teacher spread0.390 · 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 designOther design
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

Citations15
Published2018
Admission routes2
Has abstractyes

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