Family Physician Clinical Inertia in Managing Hypoglycemia
Bibliographic record
Abstract
AIMS: Clinical inertia behaviour affects family physicians managing chronic disease such as diabetes. Literature addressing clinical inertia in the management of hypoglycemia is scarce. The objectives of this study were to create a measurement for physician clinical inertia in managing hypoglycemia (ClinInert_InHypoDM), and to determine physicians' characteristics associated with clinical inertia. METHODS: The study was a secondary analysis of data provided by family physicians from the InHypo-DM Study, applying exploratory factor analysis. Principal axis factoring with an Oblimin rotation was employed to detect underlying factors associated with physician behaviors. Multiple linear regression was used to determine association between the ClinInert_InHypoDM scores and physician characteristics. RESULTS: Factor analysis identified a statistically sound 12-item one-factor scale for clinical inertia behavior. No statistically significant differences in clinical inertia score for the studied independent variables were found. CONCLUSIONS: This study provides a scale for assessing clinical inertia in the management of hypoglycemia. Further testing this scale in other family physician populations will provide deeper understanding about the characteristics and factors that influence clinical inertia. The knowledge derived from better understanding clinical inertia in primary care has potential to improve outcomes for patients with diabetes.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".