The International Universities Walking Project: Development of a framework for workplace intervention using the Delphi Technique
Bibliographic record
Abstract
Background: This paper aimed to use the Delphi technique to develop a consensus framework for a multinational, workplace walking intervention. Methods: Ideas were gathered and ranked from eight recognized and emerging experts in the fields of physical activity and health, from universities in Australia, Canada, England, the Netherlands, Northern Ireland, and Spain. Members of the panel were asked to consider the key characteristics of a successful campus walking intervention. Consensus was reached by an inductive, content analytic approach, conducted through an anonymous, three-round, e-mail process. Results: The resulting framework consisted of three interlinking themes defined as "design, implementation, and evaluation." Top-ranked subitems in these themes included the need to generate research capacity (design), to respond to group needs through different walking approaches (implementation), and to undertake physical activity assessment (evaluation). Themes were set within an underpinning domain, referred to as the "institution" and sites are currently engaging with subitems in this domain, to provide sustainable interventions that reflect the practicalities of local contexts and needs. Conclusions: Findings provide a unique framework for designing, implementing, and evaluating walking projects in universities and highlight the value of adopting the Delphi technique for planning international, multisite health initiatives.
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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 | 0.137 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| 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".