Use of eco-mapping in health services research: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: People with complex health and social needs often require care from different providers and services. Identifying their existing sources of support could assist with addressing potential gaps and opportunities for enhanced service delivery. Eco-mapping is an approach used to visually capture people's social relationships and their linkages to the larger social systems. As it is an emerging and promising approach in the health services field, a scoping review on eco-mapping is warranted. This scoping review aims to synthesise the empirical literature that has focused on the application of eco-mapping by describing characteristics, populations, methodological approaches and other features of eco-mapping in health services research. METHODS AND ANALYSIS: This scoping review will follow the Joanna Briggs Institute methodology. From the date of database construction to 16 January 2023, the following databases in English will be searched: Ovid Medline, Ovid Embase, CINAHL Ultimate (EBSCOhost), Emcare (Ovid), Cochrane Central Register of Controlled Trials (Ovid) and Cochrane Database of Systematic Reviews (Ovid) Study/Source of Evidence selection. The inclusion criteria consist of empirical literature that uses eco-mapping or a related tool in the context of health services research. Two researchers will independently screen references against inclusion and exclusion criteria using Covidence software. Once screened, the data will be extracted and organised according to the following research questions: (1) What research questions and phenomena of interest do researchers address when using eco-mapping? (2) What are the characteristics of studies that use eco-mapping in health services research? (3) What are the methodological considerations for eco-mapping in health services research? ETHICS AND DISSEMINATION: This scoping review does not require ethical approval. The findings will be disseminated through publications, conference presentations and stakeholder meetings. TRIAL REGISTRATION NUMBER: https://doi.org/10.17605/OSF.IO/GAWYN.
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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 | 0.198 | 0.163 |
| Meta-epidemiology (narrow) | 0.006 | 0.008 |
| Meta-epidemiology (broad) | 0.015 | 0.013 |
| Bibliometrics | 0.028 | 0.025 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.084 | 0.023 |
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".