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Record W4308261765 · doi:10.1002/bin.1918

The effects of a self‐management treatment package on daily step count in university students with depressive symptoms

2022· article· en· W4308261765 on OpenAlexaff
Reghann Munno, Kendra Thomson, Kimberley L. M. Zonneveld

Bibliographic record

VenueBehavioral Interventions · 2022
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsBrock University
Fundersnot available
KeywordsDepression (economics)PsychologyIntervention (counseling)Clinical psychologyDepressive symptomsConsistency (knowledge bases)Scale (ratio)Clinical Global ImpressionPhysical therapyPsychiatryMedicineAlternative medicineAnxiety

Abstract

fetched live from OpenAlex

Abstract Research demonstrates that exercise can decrease depressive symptoms, yet it is infrequently prescribed as an intervention. Self‐management techniques offer an effective and cost‐efficient approach to increase engagement in physical activity. The purpose of this study was to evaluate the efficacy of goal setting, self‐monitoring, and feedback for increasing daily step count in university students (N = 4) reporting depressive symptoms. The treatment was efficacious for increasing steps for three participants with varying levels of consistency. All participants showed a decrease in some depression symptoms on the University Student Depression Inventory. Expert ratings on the Clinical Global Impression Scale indicated improvement in global functioning for three participants. Additional research is needed to determine the efficacy of this intervention package for increasing daily steps and the relation to depression symptoms.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.034
GPT teacher head0.356
Teacher spread0.321 · 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 designNon-randomized trial
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
Published2022
Admission routes1
Has abstractyes

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