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Record W4399303747 · doi:10.1080/02703181.2024.2349822

Effects of Pulmonary Rehabilitation with Occupational Therapy on Occupational Performance

2024· article· en· W4399303747 on OpenAlexaboutno aff
Amanda McCowan, Louise Gustafsson, Michelle Bissett, Rachel Wenke, Krishna Bajee Sriram

Bibliographic record

VenuePhysical & Occupational Therapy In Geriatrics · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Wellbeing Research
Canadian institutionsnot available
Fundersnot available
KeywordsOccupational therapyRehabilitationPhysical therapyMedicinePulmonary rehabilitationPhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

Aims 1) Explore occupational challenges of individuals participating in pulmonary rehabilitation (PR), and 2) examine impacts of occupational therapy, embedded within PR, on performance of, satisfaction with, dyspnea in and experience of challenging occupations.Methods A mixed methods cohort study recruited adults from a 8-week community-based PR program which incorporated targeted occupational therapy. Participant perspectives of the occupational therapy were explored using the Canadian Occupational Performance Measure (COPM) and Modified Borg Dyspnea Scale.Results Seventeen participants with either Chronic Obstructive Pulmonary Disease, Bronchiectasis, or Interstitial Lung Disease were recruited (age 71 ± 7(SD) identifying 269 problematic occupations. Nine participants completed the program obtaining clinically and statistically significant improvements in COPM performance, satisfaction scores and Modified Borg Dyspnea Scale, maintained at 12 wk, and validated through participants reporting they ‘now do things differently’.Conclusion People with chronic respiratory conditions are occupational beings. Occupational therapy embedded within PR can influence participants’ engagement in challenging occupations.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.052
GPT teacher head0.445
Teacher spread0.393 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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