Hydration education: developing, piloting and evaluating a hydration education package for general practitioners
Bibliographic record
Abstract
OBJECTIVES: To (1) assess the hydration knowledge, attitudes and practices (KAP) of doctors; (2) develop an evidence-based training package; and (3) evaluate the impact of the training package. DESIGN: Educational intervention with impact evaluation. SETTING: Cambridgeshire, UK. PARTICIPANTS: General practitioners (GPs (primary care physicians)). INTERVENTIONS: Hydration and healthcare training. MAIN OUTCOME MEASURES: Hydration KAP score before and immediately after the training session. RESULTS: Knowledge gaps of doctors identified before the teaching were the definition of dehydration, European Food Safety Authority water intake recommendations, water content of the human body and proportion of water from food and drink. A face-to-face teaching package was developed on findings from the KAP survey and literature search. 54 questionnaires were completed before and immediately after two training sessions with GPs. Following the training, total hydration KAP scores increased significantly (p<0.001; median (25th, 75th centiles); 32 (29, 34)). Attendees rated the session as excellent or good (90%) and reported the training was likely to influence their professional practice (100%). CONCLUSIONS: The training package will continue to be developed and adapted, with increased focus on follow-up strategies as well as integration into medical curricula and standards of practice. However, further research is required in the area of hydration care to allow policymakers to incorporate hydration awareness and care with greater precision in local and national policies.
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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 | 0.027 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".