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Record W4232502253 · doi:10.1037/e676842011-003

Humor styles and negative affect as predictors of different components of physical health

2009· dataset· en· W4232502253 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePsycEXTRA Dataset · 2009
Typedataset
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsAffect (linguistics)PsychologyPhysical healthPhysical activitySocial psychologyMedicineCommunicationPhysical medicine and rehabilitationMental healthPsychiatry

Abstract

fetched live from OpenAlex

The extent to which humor and negative affect each predict different components of physical health was examined by having 105 participants complete measures of four distinct humor styles, negative affect, and three indices of physical health.An increased number of physical symptoms and more negative attitudes about illness were associated with higher levels of negative affect, but were unrelated to the humor styles.Conversely, three of the humor styles significantly predicted coping strategies for physical ailments and complaints, whereas negative affect did not.Adaptive selfenhancing humor was associated with facilitative coping strategies such as changing perspective, planning, and the effective use of humor.Maladaptive aggressive humor was linked to a more dysfunctional coping pattern that included greater denial and a reduction in the ability to change perspective.These findings reinforce the need to consider more complex models of humor that explicitly address the effects of both adaptive and maladaptive humor styles across a broad range of physical health measures while also considering effects that may be attributable to other highly-relevant attributes, such as negative affect.Key Words: humor, negative

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.029
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
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 head0.035
GPT teacher head0.385
Teacher spread0.350 · 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