GPs' approach to insulin prescribing in older patients: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Evidence suggests that insulin is under-prescribed in older people. Some reasons for this include physician's concerns about potential side-effects or patients' resistance to insulin. In general, however, little is known about how GPs make decisions related to insulin prescribing in older people. AIM: To explore the process and rationale for prescribing decisions of GPs when treating older patients with type 2 diabetes. DESIGN OF STUDY: Qualitative individual interviews using a grounded theory approach. SETTING: Primary care. METHOD: A thematic analysis was conducted to identify themes that reflected factors that influence the prescribing of insulin. RESULTS: Twenty-one GPs in active practice in Ontario completed interviews. Seven factors influencing the prescribing of insulin for older patients were identified: GPs' beliefs about older people; GPs' beliefs about diabetes and its management; gauging the intensity of therapy required; need for preparation for insulin therapy; presence of support from informal or formal healthcare provider; frustration with management complexity; and GPs' experience with insulin administration. Although GPs indicated that they would prescribe insulin allowing for the above factors, there was a mismatch in intended approach to prescribing and self-reported prescribing. CONCLUSION: GPs' rationale for prescribing (or not prescribing) insulin is mediated by both practitioner-related and patient-related factors. GPs intended and actual prescribing varied depending on their assessment of each patient's situation. In order to improve prescribing for increasing numbers of older people with type 2 diabetes, more education for GPs, specialist support, and use of allied health professionals is needed.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".