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Record W7097968010

Article The Physical Activity Scale for the Elderly (PASE) Questionnaire; Does It Predict Physical Health?

2013· article· en· W7097968010 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsnot available
Fundersnot available
KeywordsPhysical activityScale (ratio)WaistRegression analysisCardiovascular healthObesityLinear regression
DOInot available

Abstract

fetched live from OpenAlex

Abstract: A lack of physical activity is common in older adults. With the increasing Canadian senior population, identifying the minimum amount of physical activity required to maintain the health of older adults is essential. This study determined whether relationships existed between the Physical Activity Scale for the Elderly (PASE) questionnaire scores and health-related measurements in community-dwelling older adults who were meal delivery volunteers. Based on observed relationships between PASE scores and health parameters, the study attempted to predict an optimal PASE score that would ensure health parameters fell in desired ranges for older adults. 297 community-dwelling older adults (61.3 % female) 60–88 years (72.1 ± 6.5) completed the PASE and were measured for body composition, cardiovascular and blood parameters, flexibility, and handgrip strength. Significant regression models using PASE were produced for the health-related measures, but the relationships were not meaningful due to low predictive capacity. However, correlational data suggested that a minimum PASE score of ~140 for males and ~120 for females predicted a favorable waist circumference. In conclusion,

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)0.005
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.029
GPT teacher head0.338
Teacher spread0.310 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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