Bibliographic record
Abstract
AIMS: This article presents several findings of a study, conducted between 1996 and 1998, to investigate self-care decision making in diabetes. RATIONALE: The underlying assumption of many practitioners is that an invitation to people with chronic illness to participate as equal partners is sufficient to guarantee their empowerment. DESIGN: Using grounded theory, the research examined self-care decision making using a convenience sample of 22 Canadian adults with longstanding type 1 diabetes nominated as expert self-care managers. Participants audiotaped their decision making as it occurred for 3 weeks over the course of one calendar year. These audio-recordings were followed by an interview to clarify participants' decision making and factors that affected their decisions. FINDINGS: Participants identified several covert and subtle ways that practitioners contradict their stated goal of empowerment in their interactions with diabetics. Participants revealed that despite their intention to foster participatory decision making, practitioners frequently discount the experiential knowledge of diabetes over time and do not provide the resources necessary to make informed decisions. CONCLUSION: The article concludes with a discussion of the implications of the findings for practice.
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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.095 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".