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Record W2093167461 · doi:10.3109/02770901003611462

Perceptions About Self-Management Among People with Severe Asthma

2010· article· en· W2093167461 on OpenAlexaff
Beverly Williams, Gail Low, Dilini Vethanayagam

Bibliographic record

VenueJournal of Asthma · 2010
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAsthmaMedicineReferralFamily medicinePopulationAsthma managementPerceptionPhysical therapyEnvironmental healthPsychologyInternal medicine

Abstract

fetched live from OpenAlex

AIM: The purpose of this study was to explore the perceptions about self-management among people who were being followed up in a severe asthma clinic by asthma specialists for confirmed, overall severe asthma. Such insight informs how best to tailor programs for this difficult to treat patient population. METHOD: In-depth tape-recorded interviews of eight adults with severe asthma were transcribed and analyzed for salient themes using content analysis. RESULTS: To self-manage their illness, over time participants sought asthma information from a variety of sources that they often viewed as inadequate due to lack of scope and or plain language. The most valued sources of asthma information were encountered after referral to an asthma specialist and were health professionals and a pulmonary rehabilitation program. CONCLUSION: There is a need to examine the content of asthma information sources for their relevance to and influence on the behavior of patients with severe asthma.

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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.235
Teacher spread0.232 · 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 designQualitative
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

Citations17
Published2010
Admission routes1
Has abstractyes

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