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Record W6926657885 · doi:10.25384/sage.c.6007488.v1

Description and Functional Benefits of Meeting Frequency, Intensity, and Time of Resistance and Cardiovascular Exercises: A Study of Older Adults in a Community-Based, Slow-Stream Rehabilitation, Hospital-to-Home Transition Program

2022· other· en· W6926657885 on OpenAlexaff

Bibliographic record

VenueSage Journals Data · 2022
Typeother
Languageen
FieldChemistry
TopicSynthesis of Tetrazole Derivatives
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSports medicineResistance trainingRehabilitationCohortCardiovascular fitnessDescriptive statisticsGeriatricsCohort study

Abstract

fetched live from OpenAlex

This prospective cohort study described cardiovascular and resistance exercises completed by older adults in a community-based, slow-stream rehabilitation, hospital-to-home transition program; compared exercises completed to the American College of Sports Medicine (ACSM) exercise guidelines; and, assessed differences in Late Life Function and Disability Index (LLFDI)-Function Component (FC) between older adults who met and did not meet the ACSM guidelines. Descriptive statistics and Factorial ANCOVA were conducted. For cardiovascular exercise 59.3% of participants met frequency, 73.4% met intensity, and 35.9% met time. For resistance exercise, 67.2% of participants met frequency, 42.2% met intensity, and 76.6% number of repetitions. Participants who met both frequency and time for cardiovascular exercise had higher LLFDI-FC scores, as did those who met intensity and/or number of repetitions for resistance exercise. The findings provide support that older adults engaged in a slow-stream rehabilitation program can meet the ACSM exercise guidelines for community-dwelling older adults, and that meeting the guidelines improves function.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.020
GPT teacher head0.246
Teacher spread0.226 · 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.

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
Published2022
Admission routes1
Has abstractyes

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