Hong Kong Healthspan Project - A Study Protocol
Bibliographic record
Abstract
Objectives: Despite the rapid uptake of telehealth for the delivery of nutrition care in recent years and the predicted future demand, there remains a deficit in telehealth integration into didactic education within the nutrition and dietetics curriculum.Competency-based education is a crucial aspect of the education of future workforces.Thus, there is a need for consensus on essential competencies and skills for nutrition and dietetics practitioners.The study aims to apply a modified e-Delphi process to achieve expert consensus on the essential telehealth competencies and skills required for nutrition and dietetics practitioners to provide safe and effective virtual nutrition care.Methods: A consensus-building study will be conducted using a modified e-Delphi technique.The Delphi method uses a blinded, structured, and iterative process to generate expert opinion.First, an initial list of potentially relevant telehealth competencies for nutrition practice will be identified based on a focused literature review.Then, three iterative rounds of anonymous online surveys will be conducted with a panel of virtual nutrition care experts (n¼30) to refine and reach a consensus for each item.Consensus will be defined a priori as >80% of experts identifying a topic as very important.The online collaborative research platform, Surveylet, will facilitate the modified e-Delphi process and data analysis. Results: N/A (study protocol).Conclusions: Findings from the study can be used to guide telehealth curriculum in dietetics education and professional development and, ultimately, improve virtual nutrition care services and outcomes.
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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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.072 | 0.018 |
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".