Fuzzy cognitive mapping: An old tool with new uses in nursing research
Bibliographic record
Abstract
AIMS: Describe the implementation and uses of fuzzy cognitive mapping (FCM) as a constructive method for meeting the unique and rapidly evolving needs of nursing inquiry and practice. DESIGN: Discussion paper. DATA SOURCES: Drawing on published scholarship of cognitive mapping from the fields of ecological management, information technology, economics, organizational behaviour and health development, we consider how FCM can contribute to contemporary challenges and aspirations of nursing research. IMPLICATIONS FOR NURSING: Fuzzy cognitive mapping can generate theory, describe knowledge systems in comparable terms and inform questionnaire design and dialogue. It can help build participant-researcher partnerships, elevate marginalized voices and facilitate intercultural dialogue. As a relatively culturally safe and foundational approach in participatory research, we suggest that FCM should be used in settings of transcultural nursing, patient engagement, person- and family-centred care and research with marginalized populations. FCM is amenable to rigorous analysis and simultaneously allows for greater participation of stakeholders. CONCLUSION: In highly complex healthcare contexts, FCM can act as a common language for defining challenges and articulating solutions identified within the nursing discipline. IMPACT: There is a need to reconcile diverse sources of knowledge to meeting the needs of nursing inquiry. FCM can generate theory, describe knowledge systems, facilitate dialogue and support questionnaire design. In its capacity to engage multiple perspectives in defining problems and identifying solutions, FCM can contribute to advancing nursing research and practice.
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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 | 0.041 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".