Use of Ecomaps in Qualitative Health Research
Bibliographic record
Abstract
Qualitative health research plays a central role in exploring individuals’ experiences and perceptions of wellness, illness, and healthcare services. Visual tools are increasingly used for data elicitation. An ecomap is a visual tool that applies ecosystems theory to human communities and relationships to provide an illustration of the quality of relationships. We describe the use of ecomaps in qualitative health research. Searches across eight databases identified 407 citations. We screened them in duplicate to identify 129 publications that underwent full text review and included 73 in the final synthesis. We classified and summarized data based on iterative comparisons across sources. Benefits of using ecomaps include improving rapport and engagement with study participants, facilitating iterative question development, and highlighting the social contexts of relationships. When used in conjunction with interviews, they promote data credibility through triangulation. Investigators have used ecomaps as a tool to facilitate primary and secondary analysis of data. Researchers have adapted the ecomap to meet their health research needs. Challenges to their use include additional time and training needed to complete, and potential privacy and confidentiality concerns. Ecomaps can be useful in qualitative health research to enhance data elicitation, analysis, presentation, and to augment study rigor.
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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 | 0.290 | 0.442 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.038 | 0.048 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".