Exploring the Landscape of Eco‐Mapping in Health Services Research: A Comprehensive Review
Bibliographic record
Abstract
Health services research is important in improving health systems’ and providers’ efficiency and effectiveness. This may require health services to intervene at an individual and community level to address people’s complex social issues. An important issue is social connections, which have been identified as a social determinant of health and can help buffer stressful life events. Social support networks can be visualized using eco‐maps, a tool that originated in child welfare practices and has been adopted widely by clinicians and researchers. This paper aims to understand where and how eco‐maps have been used in health services research. To answer the research questions, this scoping review used the Joanna Briggs Institute guidelines for scoping reviews. In total, 70 studies were included in the scoping review. The authors found that social support denoted in an eco‐map does not guarantee the provision of support; however, the dialogue needed to create an eco‐map could facilitate conversations about care expectations, identification of vulnerable points or risk factors, and actions to improve family and individual functioning. A significant gap remains in the knowledge and use of eco‐maps in identifying population service and resource gaps and how to bridge the knowledge‐to‐action chasm better. Further exploration is needed to examine how to optimize the application of eco‐mapping in the health services context, including generating guidelines, templates, or instructions for implementation. Therefore, addressing this gap is vital for ensuring eco‐mapping informs future service design and policy changes.
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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 | 0.039 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.027 | 0.034 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".