Implementation of Physical Activity Interventions in Rural, Remote, and Northern Communities: A Scoping Review
Bibliographic record
Abstract
Compared with urban centers, rural, remote, and northern communities face substantial health inequities and increased rates of noncommunicable disease fuelled, in part, by decreased participation in physical activity. Understanding how the unique sociocultural and environmental factors in rural, remote, and northern communities contribute to implementation of physical activity interventions can help guide health promotion policy and practice. A scoping review was conducted to map literature describing the implementation of physical activity interventions in rural, remote, and/or northern communities. Databases MEDLINE, PsycINFO, EMBASE, CINAHL, and SPORTDiscus were searched using a predetermined search strategy. Outcomes of interest included community demographics, program characteristics, intervention results, measures of implementation, and facilitators or barriers to implementation. A total of 1672 articles were identified from a search of databases, and 8 from a targeted hand search. After screening based on inclusion and exclusion criteria, 12 articles were summarized in a narrative review. Prominent barriers to physical activity program implementation included transportation, lack of infrastructure, sociocultural factors, and weather. Facilitators of program success included flexibility and creativity on the part of the implementation team, leveraging community relationships, and shared resources. Few papers reported on traditional implementation outcomes such as fidelity, dose, and quality. There is a lack of rigorous implementation evaluations of physical activity interventions delivered in rural, remote, or northern communities. Positive aspects of rural life, such as social cohesion and willingness to share resources, appear to contribute to successful program implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".