"I take what I think works for me": a qualitative study to explore patient perception of diabetes treatment benefits and risks.
Bibliographic record
Abstract
BACKGROUND: Diabetes is impacting more and more people each year. A key aspect of disease management is patient adherence to prescribed treatments. Treatment adherence is influenced by many factors, including the understanding of a treatment's benefits and risks. OBJECTIVE: This study sought to describe the experience of benefit and risk assessment for people with type 2 diabetes when making treatment decisions. METHODS: This study utilized qualitative research methods. Individual interviews were conducted using a semi-structured interview guide. Both purposeful and theoretical sampling was used. A grounded theory approach was employed to facilitate data collection and analysis. RESULTS: The 18 study participants were on varying treatment regimens for diabetes (diet therapy, oral medications, and insulin). Many people felt that they had not received enough information about the benefits and risks of treatment at the point of decision-making and later sought this information on their own. Participants did not seem to consciously assess treatment benefits and risks when treatments were prescribed or suggested, but rather continued to make decisions after the clinical encounter by means of experimentation or experience with treatments. In general, benefits and risks were conceptualized very broadly, and some people were not able to verbally articulate their perceptions of treatment benefits and risks. CONCLUSION: Patients' assessment of treatment benefits and risks is an ongoing, often unconscious process that requires continuous interaction with the health care system. Access to information and an opportunity to discuss treatment options with health care providers are important to people with diabetes when making treatment decisions.
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".