Healthcare professional support: diabetic ketoacidosis avoidance and care
Bibliographic record
Abstract
Diabetic ketoacidosis is an acute, life-threatening diabetes-related emergency. Our study aimed to explore the support that key healthcare professionals desired around diabetic ketoacidosis avoidance and care within a low socio economic region of Australia. Participants were recruited from a community hospital using the following methods: direct contact of key hospital staff and use of snowballing, posters placed in clinical and non-clinical areas. Audio-taped interview data were analysed using Gibbs’s thematic framework, which entails transcription and familiarisation, code building, theme development, and data consolidation and interpretation. Interviews were conducted with 15 healthcare professionals from across allied health and medical professions. Describing factors relating to diabetic ketoacidosis presentations in people with type 1 diabetes, two themes emerged: a disparity in knowledge and health system opportunities. Findings complement the wealth of literature which details the problem of gaps in support for patient self-care to avoid diabetic ketoacidosis and prevent late presentation of this potentially fatal condition. There is a pressing need to ensure that healthcare professionals have the appropriate level of knowledge to prevent, recognise and treat diabetic ketoacidosis. Service reconfiguration can support care delivery.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".