3 themes described how self care management was learned and experienced by patients with chronic illness
Bibliographic record
Abstract
Thorne S, Paterson B, Russell C. The structure of everyday self-care decision making in chronic illness. Qual Health Res 2003;13:1337–52.[OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] Q In patients with expertise in self care management of chronic illnesses (ie, type 1 diabetes, type 2 diabetes, HIV/AIDS, or multiple sclerosis), how was the everyday self care decision making process learned and experienced? Qualitative secondary analysis of data from 2 primary studies. The 2 primary studies included patients from British Columbia, Canada. 43 patients (22 with type 1 diabetes, and 7 each with type 2 diabetes, HIV/AIDS, and MS) who had been chronically ill and had several years of experience in self care decision making for their disease. In both primary studies, data were collected through individual interviews at baseline, and after 2–3 audiotaped think aloud sessions (each lasting 1 week) over the course of 12 months; and focus group interviews done near the end of … [1]: {openurl}?query=rft.jtitle%253DQualitative%2BHealth%2BResearch%26rft.stitle%253DQual%2BHealth%2BRes%26rft.aulast%253DThorne%26rft.auinit1%253DS.%26rft.volume%253D13%26rft.issue%253D10%26rft.spage%253D1337%26rft.epage%253D1352%26rft.atitle%253DThe%2BStructure%2Bof%2BEveryday%2BSelf-Care%2BDecision%2BMaking%2Bin%2BChronic%2BIllness%26rft_id%253Dinfo%253Adoi%252F10.1177%252F1049732303258039%26rft_id%253Dinfo%253Apmid%252F14658350%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=10.1177/1049732303258039&link_type=DOI [3]: /lookup/external-ref?access_num=14658350&link_type=MED&atom=%2Febnurs%2F7%2F3%2F94.atom [4]: /lookup/external-ref?access_num=000186446000002&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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".