What is Mental Health Nursing Anyway? Advantages and Issues of Utilizing Duoethnography to Understand Mental Health Nursing
Bibliographic record
Abstract
In recent decades scholars have begun to question the value of mental health nursing. The term has lost both conceptual and explanatory power in the modern globalized world in which multidisciplinary teams now carry out many functions once unique to the specialization, yet its distinction persists. The purpose of this paper is to explore an emerging research methodology, duoethnography, as an avenue to revive mental health nursing, by subverting the dominant post-positivist, scientifically driven, medically framed, evidence-based practice perspective, to gain greater understanding of the nuances of mental health nursing practice. Duoethnography offers promise in challenging nursing research norms embedded in an empirically based medical model, however the newness of the method poses potential methodological issues. Duoethnography is a methodology well-suited to explore the question of whether mental health nursing is an outmoded tradition too deeply entrenched in the institutional past, or an emerging profession leading mental health care.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.082 |
| Scholarly communication | 0.020 | 0.024 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".