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Record W1967880068 · doi:10.1080/14768320601176089

Structured writing about current stressors: The benefits of developing plans

2007· article· en· W1967880068 on OpenAlexaff
Olivia T. Lestideau, Loraine F. Lavallee

Bibliographic record

VenuePsychology and Health · 2007
Typearticle
Languageen
FieldPsychology
TopicMental Health via Writing
Canadian institutionsUniversity of Northern British ColumbiaUniversity of Alberta
Fundersnot available
KeywordsStressorFeelingAffect (linguistics)PsychologyControl (management)Social psychologyApplied psychologyClinical psychologyComputer scienceCommunication

Abstract

fetched live from OpenAlex

To investigate the health outcomes of structured writing about everyday stressors, 64 undergraduates selected a stressful event with which they were currently dealing, and wrote about it at home on three occasions in 1 week. Two forms of writing were manipulated: expressive writing – exploring one's thoughts and feelings about the stressor; and planful writing – developing plans to deal with the problem. Appraisals of control and efficacy were investigated as mediators. Expressive writing yielded no health benefits and did not affect appraisals, but planful writing did. Whereas non-planners' levels of negative affect (NA) remained stable across writing days, planners initially experienced higher NA, but their NA decreased significantly across writing days. Planful writers, relative to non-planners, felt less control over their emotions and less confidence in resolving their problem, but it was non-planners who experienced an increase in stress-related symptoms following writing. Appraisals did not mediate the physical health outcomes.

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)0.016
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.124
GPT teacher head0.480
Teacher spread0.356 · 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 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

Citations7
Published2007
Admission routes1
Has abstractyes

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