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Record W2996086989 · doi:10.5539/gjhs.v12n1p1

Health Psychological Case Study of High Intensity, Low Impact, Physical Training Program

2019· article· en· W2996086989 on OpenAlexvenueaboutno aff
Stephen D. Edwards

Bibliographic record

VenueGlobal Journal of Health Science · 2019
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyExperiential learningApplied psychologyPsychological resilienceMoodTest (biology)Medical educationClinical psychologyMedicineSocial psychologyPedagogy

Abstract

fetched live from OpenAlex

This case study reports on the health psychological evaluation of an integrated, high intensity, lo impact, physical training program for a 31 year old, male accountancy teacher. High intensity components consisted of running, cycling and the Canadian Airforce 5BX program. Low impact components comprised walking and Chi-gung inspired Pilates. Squash and swimming were also occasional activities. The pre-test and post-test, process and outcome evaluative, research design included qualitative and quantitative components in the form of psychometric testing, diarizing of physical activity, experiential descriptions of the various components and program outcome evaluation. Quantitative findings indicated significant improvements in psychophysiological coherence, mood, resilience and general health. Qualitative experiential descriptions provided further evidence of health psychological growth. Integrative findings emphasize the importance of physical training programs based on client individual preferences. The client reported that he found the program to be enjoyable, flexible, invigorating and readily adaptable to suit the needs of various individuals who face time and space constraints in their daily lives. With minimal innovation for greater complexity, control and/or challenges, such programs may readily yield enjoyable flow experiences.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Case reportlow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

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)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.080
GPT teacher head0.533
Teacher spread0.453 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designCase report · Other design
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
Published2019
Admission routes2
Has abstractyes

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