How to find lessons from the public health literature: Example of a scoping study protocol on the neighborhood environment
Bibliographic record
Abstract
BACKGROUND: As key determinants of many favorable health and quality of life outcomes, it is important to identify factors associated with mobility and social participation. Although several investigations have been carried out on mobility, social participation and neighborhood environment, there is no clear integration of these results. This paper presents a scoping study protocol that aims to provide a comprehensive understanding of how the physical and social neighborhood environment is associated with or influences mobility and social participation in older adults. METHODS: The rigorous methodological framework for scoping studies is used to synthesize and disseminate current knowledge on the associations or influence of the neighborhood environment on mobility and social participation in aging. Nine databases from public health and other fields are searched with 51 predetermined keywords. Using content analysis, all data are exhaustively analyzed, organized, and synthesized independently by two research assistants. DISCUSSION: A comprehensive synthesis of empirical studies provides decision-makers, clinicians and researchers with current knowledge and best practices regarding neighborhood environments with a view to enhancing mobility and social participation. Such a synthesis represents an original contribution and can ultimately support decisions and development of innovative interventions and clear guidelines for the creation of age-supportive environments. Improvements in public health and clinical interventions might be the new innovation needed to foster health and quality of life for aging population. Finally, the aspects of the associations or influence of the neighborhood environment on mobility and social participation not covered by previous research are identified. CONCLUSIONS: Among factors that impact mobility and social participation, the neighborhood environment is important since interventions targeting it may have a greater impact on an individual's mobility and social participation than those targeting individual factors. Although investigations from various domains have been carried out on this topic, no clear integration of these results is available yet.
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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 | 0.186 | 0.199 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.015 | 0.008 |
| Insufficient payload (model declined to judge) | 0.090 | 0.026 |
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".