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Record W2067206954 · doi:10.4236/psych.2014.519217

Can Physical Activity Improve Depression, Coping & Motivation to Exercise in Children and Youth Experiencing Challenges to Mental Wellness?

2014· article· en· W2067206954 on OpenAlexaff
Scott Oddie, Denise Fredeen, Brandy Williamson, Drew DeClerck, Sacha Doe, Kelly Moslenko

Bibliographic record

VenuePsychology · 2014
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsRed Deer PolytechnicAlberta Health Services
Fundersnot available
KeywordsPsychologyMental healthCoping (psychology)MoodPsychosocialPhysical activityClinical psychologyDistractionDevelopmental psychologyPsychiatryPhysical therapyMedicine

Abstract

fetched live from OpenAlex

This study examined the influence of a physical activity (PA) program (Move Your Mood) on children and adolescents receiving services in community mental health clinics. Participants (N = 35) were referred to the (PA) program by their mental health therapist. Coaches engaged participants in individual one-on-one and group activity sessions for eight weeks. Participant heart rates were monitored during physical activity sessions and designed to achieve moderate to high intensity. Participants reported significant improvements in mood immediately following physical activity. Measures of motivation to exercise, coping, and depression were taken before program participation, at 4-weeks, and at completion of the 8 week program. Results indicate that the PA program significantly improved child and adolescent ability to cope as well as their intrinsic motive to exercise. In addition, the PA program significantly reduced self-reported depressive symptoms. Qualitative analysis indicates that social supports and enhanced self-efficacy resulting from physical activity engagement and sessions are key factors associated with program outcomes. The current study provides evidence to support three key psychosocial theories: social interaction, distraction hypothesis, and mastery hypothesis.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score0.799

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.036
GPT teacher head0.347
Teacher spread0.312 · 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 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

Citations6
Published2014
Admission routes1
Has abstractyes

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