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Record W3126550531 · doi:10.7870/cjcmh-2020-017

Consumer Evaluation of the Jump Step Program for People with Mood Disorders: Implications for Program Development

2020· article· en· W3126550531 on OpenAlexaffvenue
Neville Li, Joanie Sims‐Gould, Sara Vazirian, Karim Khan

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2020
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMoodMental healthPsychologyVariety (cybernetics)Mood disordersApplied psychologyClinical psychologyMedical educationMedicinePsychiatryComputer science

Abstract

fetched live from OpenAlex

An extensive body of literature supports the mental health benefits of exercise. Some clinicians are starting to embrace the concept “exercise is medicine.” In our study, we evaluated longitudinal qualitative data from participants with a mood disorder who participated in an exercise program. Recommendations from participants include providing a program that offers a variety of exercise options supported by professionals such as an exercise specialist in a community setting. Researchers and clinicians can take our results into account when designing or running similar programs for people with mood disorders.

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)0.033
metaresearch head score (Gemma)0.078
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.175

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.078
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0130.001

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 head0.134
GPT teacher head0.411
Teacher spread0.277 · 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

Citations1
Published2020
Admission routes2
Has abstractyes

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