Qualitative–Geospatial Methods of Exploring Person–Place Transactions in Aging Adults: A Scoping Review
Bibliographic record
Abstract
Purpose: Research exploring how places shape and interact with the lives of aging adults must be grounded in the places where aging adults live and participate. Combined participatory geospatial and qualitative methods have the potential to illuminate the complex processes enacted between person and place to create much-needed knowledge in this area. The purpose of this scoping review was to identify methods that can be used to study person-place relationships among aging adults and their neighborhoods by determining the extent and nature of research with aging adults that combines qualitative methods with participatory geospatial methods. Design and Methods: A systematic search of nine databases identified 1,965 articles published from 1995 to late 2015. We extracted data and assessed whether the geospatial and qualitative methods were supported by a specified methodology, the methods of data analysis, and the extent of integration of geospatial and qualitative methods. Results: Fifteen studies were included and used the photovoice method, global positioning system tracking plus interview, or go-along interviews. Most included articles provided sufficient detail about data collection methods, yet limited detail about methodologies supporting the study designs and/or data analysis. Implications: Approaches that combine participatory geospatial and qualitative methods are beginning to emerge in the aging literature. By more explicitly grounding studies in a methodology, better integrating different types of data during analysis, and reflecting on methods as they are applied, these methods can be further developed and utilized to provide crucial place-based knowledge that can support aging adults' health, well-being, engagement, and participation.
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 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 | 0.087 | 0.216 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.032 | 0.029 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".