Establishing Trustworthiness in Health Care Process Modelling: A Practical Guide to Quality Enhancement in Studies Using the Functional Resonance Analysis Method
Bibliographic record
Abstract
The Functional Resonance Analysis Method (FRAM) is a novel healthcare research methodology that has increasingly been applied in the healthcare domain. The method has an ability to map and model everyday healthcare activities and their interdependencies, as well as, demonstrate how variability can emerge and impact system outcomes. To build a FRAM model, researchers gather data from key stakeholders, such as health care workers and patients and their families using qualitative data collection methods. An important consideration for researchers using the FRAM is how they will establish trustworthiness in their study findings given the data used to build and analyze a FRAM model can be subjective. In the spirit of advancing the quality of qualitative research, the aim of this paper is to provide practical guidance to researchers on how to employ quality enhancement criteria and strategies in their qualitative research efforts so that the resulting FRAM models and insights afforded by them are trustworthy.
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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 | 0.400 | 0.436 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.009 | 0.031 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.010 | 0.016 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".