Self-Management Experience of Nurses Living with Migraine: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Migraine is a neurobiological condition characterized by a constellation of unpredictable symptoms and is the second cause of disability worldwide. Migraine is prevalent among nurses. However, literature exploring nurses' experience of living with migraine is scarce which has important individual and systems implications for health and wellness and patient safety. Self-management is essential in chronic disease management as the patient engages in various strategies to be able to live with their condition. PURPOSE: This study explored the experiences of living and working with migraine among female nurses in Ontario, with particular attention to their priorities and strategies for self-management. METHODS: Interpretive description methodology was employed to guide this study and informed a thematic analysis approach to examine the self-management experiences of nurses living with migraine. RESULTS: Nurses engaged in various self-management strategies including pharmacological and non-pharmacological strategies and highlighted the role of technology in migraine self-management. Participants described experiences of living with migraine as an invisible condition including feelings of not being understood, stigmatization, and the absence of formal support at the workplace. CONCLUSION: The implications of these findings support the incorporation of a critical approach to relational engagement that is person-centred including nonjudgemental, strength-based care as a practice approach when caring for persons living with migraines and the need to include experiential learning in educational curriculums as a strategy to reduce stigma against migraines.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".