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Record W2000557009 · doi:10.1136/ebn.8.4.127

Community living older adults described using medical, collaborative, and self agency models for asthma self management

2005· letter· en· W2000557009 on OpenAlexaff
Carol Jillings

Bibliographic record

VenueEvidence-Based Nursing · 2005
Typeletter
Languageen
FieldHealth Professions
TopicHealth, psychology, and well-being
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAsthmaMedicineSelf-managementFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

Koch T, Jenkin P, Kralik D. Chronic illness self-management: locating the ‘self’. J Adv Nurs 2004;48:484–92.[OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] Q How do community living older adults with asthma describe asthma self management models? Qualitative study using indepth interviews, an open ended questionnaire, and participatory action research (PAR) groups. South Australia. 24 community living adults >60 years of age (67% women, mean age 76 y, age range 60–92 y) who had medically diagnosed asthma and were using, or had been prescribed, daily preventative medications. Data were collected using individual indepth interviews, an open ended questionnaire, and 2 PAR groups. Indepth interviews lasting about 1 hour each were held in participants’ homes, and guiding questions were used to help participants reflect on their personal asthma self management stories. Interviews were tape recorded, transcribed verbatim, and collaboratively analysed by 3 researchers. 18 patients and 6 invited partners participated in 2 PAR meetings to collaboratively develop a model that would enable self management of asthma for older … [1]: {openurl}?query=rft.jtitle%253DJournal%2Bof%2Badvanced%2Bnursing%26rft.stitle%253DJ%2BAdv%2BNurs%26rft.aulast%253DKoch%26rft.auinit1%253DT.%26rft.volume%253D48%26rft.issue%253D5%26rft.spage%253D484%26rft.epage%253D492%26rft.atitle%253DChronic%2Billness%2Bself-management%253A%2Blocating%2Bthe%2B%2527self%2527.%26rft_id%253Dinfo%253Adoi%252F10.1111%252Fj.1365-2648.2004.03237.x%26rft_id%253Dinfo%253Apmid%252F15533086%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1111/j.1365-2648.2004.03237.x&link_type=DOI [3]: /lookup/external-ref?access_num=15533086&link_type=MED&atom=%2Febnurs%2F8%2F4%2F127.atom [4]: /lookup/external-ref?access_num=000225369600007&link_type=ISI

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.493
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0050.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0030.007
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 head0.092
GPT teacher head0.428
Teacher spread0.336 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreCommentary

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
Published2005
Admission routes1
Has abstractyes

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