Towards a new (or rearticulated) philosophy of mental health nursing: A dialogue‐on‐dialogue
Bibliographic record
Abstract
The following dialogue takes up recent calls within nursing scholarship to critically imagine alternative nursing futures through the relational process of call and response. Towards this end, the dialogue builds on letters which we, the authors, exchanged as part of the 25th International Nursing Philosophy Conference in 2022. In these letters, we asked of ourselves and each other: If we were to think about a new philosophy of mental health nursing, what are some of the critical questions that we would need to ask? What warrants exploration? In thinking through these questions, our letters facilitated a collaborative enquiry in which philosophy and theory were generative tools for thinking beyond what is and towards what is yet to come. In this paper, we expand the dialogue within these letters-in a 'dialogue-on-dialogue'-and take up one thread of our discussion to argue that a new philosophy of mental health nursing must rethink the relationships between 'practitioner'/'self' and 'self'/'other' if it is to create a radically different future. Further, we posit solidarity and public love as possible alternatives to foregrounding the 'work' of mental health nursing. The possibilities we present here should be received as partial, contingent and unfinished. Indeed, our purpose in this paper is to provoke discussion and, in so doing, to model what we believe is a necessary shift towards criticality in our communities of nursing scholarship.
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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 | 0.045 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.021 | 0.093 |
| Scholarly communication | 0.029 | 0.025 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.016 | 0.030 |
| Insufficient payload (model declined to judge) | 0.004 | 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".