Bibliographic record
Abstract
Psychiatric/mental health nursing is often like a house divided. Even the double name commonly used to describe our specialty—psychiatric/mental health—reflects possible ambivalence or duality of purpose, or both. It should not surprise anyone that our attempts to implement evidence-based practice mirror this struggle. Mental health nurses initially defined our practice as an interpersonal therapeutic process to assist the client in growth.1–3 This was in synchrony with psychiatry's earlier focus on psychotherapy as the treatment for mental illness. The division in mental health nursing occurred when psychiatry moved to a more biological perspective. Should mental health nursing follow this route or continue to focus on counselling and process issues? Both the “therapeutic relationship” and “biology” camps have used evidence-based arguments to buttress their positions. It could be argued that all the psychiatric/mental health professions experience this struggle, but it has been a particularly open struggle in nursing. The struggle is present in our literature. Gournay's article, “Schizophrenia: a review of the contemporary literature and implications for mental health nursing theory, practice and education,” is an example of this struggle.4 The main purpose of this article was to review the literature on schizophrenia from a biological perspective, including the aetiology, epidemiology, and …
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".