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Record W2605143135 · doi:10.3390/ijerph14040384

Building Health Promotion into the Job of Home Care Aides: Transformation of the Workplace Health Environment

2017· article· en· W2605143135 on OpenAlexfundno aff
Naoko Muramatsu, Lijuan Yin, Ting‐Ti Lin

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2017
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsnot available
FundersNational Center for Chronic Disease Prevention and Health PromotionNational Institute on AgingCanadian Dairy Commission
KeywordsWorkplace health promotionPromotion (chess)Health promotionNursingTransformation (genetics)Home healthHealth careBusinessMedicinePolitical sciencePublic health

Abstract

fetched live from OpenAlex

Home care aides (HCAs), predominantly women, constitute one of the fastest growing occupations in the United States. HCAs work in clients’ homes that lack typical workplace resources and benefits. This mixed-methods study examined how HCAs’ work environment was transformed by a pilot workplace health promotion program that targeted clients as well as workers. The intervention started with training HCAs to deliver a gentle physical activity program to their older clients in a Medicaid-funded home care program. Older HCAs aged 50+ reported increased time doing the types of physical activity that they delivered to their clients (stretching or strengthening exercise) (p = 0.027). Almost all (98%) HCAs were satisfied with the program. These quantitative results were corroborated by qualitative data from open-ended survey questions and focus groups. HCAs described how they exercised with clients and how the psychosocial work environment changed with the program. Building physical activity into HCAs’ job is feasible and can effectively promote HCAs’ health, especially among older HCAs.

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)0.010
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.075
GPT teacher head0.450
Teacher spread0.374 · 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

Citations29
Published2017
Admission routes1
Has abstractyes

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