Use of geographic intelligence to investigate community participation: a scoping review
Bibliographic record
Abstract
Community participation is an important aspect of individuals’ functioning. This construct is defined as an individual’s “active involvement in activities that are intrinsically social and either occur outside the home or are part of a nondomestic role” (Chang et al., 2013, p.772). One of the dimensions of community participation is the “attendance” in activities performed outside home. Traditionally, individuals’ attendance has been measured by means of self-report questionnaires, through which many parameters can be examined. Such parameters include frequency, intensity and length of attendance, as well as the variety of activities in which an individual takes part. A promising technology that can be used to map an individual’s displacement in the community consists of geoprocessing tools and methods (Brusilovskiy et al., 2016). Researchers from several countries, such as Australia, Canada, USA, Israel and Germany, have been using geographic intelligence to understand factors related to accessibility and community integration of different clinical populations. The applicability of this technology in understanding individuals’ community participation is the main focus of this scoping review, which aims to synthesize the unique contributions of these tools and methods in measuring this construct. The results of our study may elucidate how geographic intelligence is being applied in the study of community participation and highlight the validity, meaningfulness and possible limitations of this technology’s application.
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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 | 0.023 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.037 | 0.033 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".