Lessons learned from the development and implementation of a citywide stair prompt initiative
Bibliographic record
Abstract
Stair climbing is a readily available form of vigorous-intensity physical activity. Evidence indicates that placing stair prompt signs at points-of-decision (e.g. near elevators and stairways) is an inexpensive, effective strategy for increasing physical activity through stair use. This article aims to share the experience of the New York City Department of Health and Mental Hygiene (NYC DOHMH) in the outreach and implementation of a population-scale stair prompt initiative, including lessons learned from process evaluations, with other public health authorities conducting a similar program. Between May 2008 and August 2012, NYC DOHMH implemented a stair prompt initiative as one strategy in a comprehensive program to increase physical activity and healthy eating through physical improvements to NYC's buildings, streets and neighborhoods, particularly targeting facilities in underserved and low-income neighborhoods. Program evaluation was conducted using program planning documents to examine the process, and data from NYC information line call center, outreach tracking database, and site and phone audits to examine process outcomes. The initiative successfully distributed more than 30,000 stair prompts to building owners/managers of over 1000 buildings. Keys to success included multi-sector partnerships between NYC's Health Department and non-health government agencies and organizations (such as architecture and real estate organizations), a designated outreach coordinator, and outreach strategies targeting building owners/managers owning/managing multiple buildings and buildings serving underserved and at risk populations. A NYC citywide initiative successfully distributed stair prompts to the wider community to promote population-level health impacts; lessons learned may assist other jurisdictions considering similar initiatives to increase physical activity.
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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 | 0.060 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.004 | 0.010 |
| 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".