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Record W2169199925 · doi:10.1177/1479972311433574

Self-management programmes for COPD

2012· article· en· W2169199925 on OpenAlexaff
Tanja Effing, Jean Bourbeau, Jan H. Vercoulen, Andrea J. Apter, David Coultas, Paula Meek, Paul van der Valk, Martyn R Partridge, Job van der Palen

Bibliographic record

VenueChronic Respiratory Disease · 2012
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsMcGill UniversityRoyal Victoria Hospital
Fundersnot available
KeywordsMedicineCLARITYSelf-managementPulmonary diseaseCOPDMedical educationComprehensionConformityIntervention (counseling)Process (computing)Process managementNursingPsychologyComputer scienceSocial psychology

Abstract

fetched live from OpenAlex

Self-management is of increasing importance in chronic obstructive pulmonary disease (COPD) management. However, there is confusion over what processes are involved, how the value of self-management should be determined, and about the research priorities. To gain more insight into and agreement about the content of programmes, outcomes, and future directions of COPD self-management, a group of interested researchers and physicians, all of whom had previously published on this subject and who had previously collaborated on other projects, convened a workshop. This article summarises their initial findings. Self-management programmes aim at structural behaviour change to sustain treatment effects after programmes have been completed. The programmes should include techniques aimed at behavioural change, be tailored individually, take the patient's perspective into account, and may vary with the course of the patient's disease and co-morbidities. Assessment should include process variables. This report is a step towards greater conformity in the field of self-management. To enhance clarity regarding effectiveness, future studies should clearly describe their intervention, be properly designed and powered, and include outcomes that focus more on the acquisition and practice of new skills. In this way more evidence and a better comprehension on self-management programmes will be obtained, and more specific formulation of guidelines on self-management made possible.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.002

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 head0.026
GPT teacher head0.325
Teacher spread0.298 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations177
Published2012
Admission routes1
Has abstractyes

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