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Record W2170459771 · doi:10.1093/ptj/80.10.986

Quality of Life After Pulmonary Rehabilitation: Assessing Change Using Quantitative and Qualitative Methods

2000· article· en· W2170459771 on OpenAlexaff
Pat G. Camp, Jessica Appleton, W. Darlene Reid

Bibliographic record

VenuePhysical Therapy · 2000
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPhysical therapyRehabilitationSpirometryQuality of life (healthcare)Pulmonary rehabilitationMedicinePulmonary function testingCOPDQualitative researchPulmonary diseasePhysical medicine and rehabilitationInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: The purpose of this study was to use quantitative and qualitative research methods to evaluate quality-of-life (QOL) changes in patients with chronic obstructive pulmonary disease after pulmonary rehabilitation. SUBJECTS: Twenty-nine individuals with COPD (18 women and 11 men), with a mean age of 69 years (SD=8.6, range=53-92), participated. METHODS: Subjects were assessed before and after a 5-week control phase and after a 5-week rehabilitation phase using the Chronic Respiratory Questionnaire (CRQ), the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36), and spirometry. Our qualitative research was based on a subsample of 7 subjects who were interviewed after pulmonary rehabilitation. RESULTS: Pulmonary rehabilitation improved QOL, as demonstrated by increases of 22% and 14% in the physical function categories of the CRQ and the SF-36, respectively, and by an increase of 10% in the CRQ's emotional function category. The qualitative data indicated how pulmonary rehabilitation influenced QOL. CONCLUSION AND DISCUSSION: The use of both quantitative and qualitative methods illustrated the nature of improvement in QOL after pulmonary rehabilitation. Improved physical function, less dyspnea, and a heightened sense of control over the subjects' COPD resulted in increased confidence and improved emotional well-being.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.928
Threshold uncertainty score0.766

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.241
GPT teacher head0.535
Teacher spread0.294 · 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.

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

Citations42
Published2000
Admission routes1
Has abstractyes

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