Invitations to think and feel in forensic nursing; the role of clinical supervision and reflective practice
Bibliographic record
Abstract
Providing nursing care to people who have experienced child sexual abuse, assault and rape is a highly specialised and psychologically demanding task. Necessarily much focus is on the technical aspects of the task of providing care to patients. The specialist administrative and nursing team in the Sexual Assault and Treatment Units (SATUs) in Ireland provide complex treatment to a particularly vulnerable group of people from various backgrounds in Irish Society. The service is open to all genders and gender identities, aged 14 and over. The care is free and it is a recognised safe place to go to if you have been raped or sexually assaulted. In the Department of Health’s Policy Review of the SATUs in Ireland they recognised the challenging nature of the work and recommended the provision of high quality emotional supports for all staff (core and on-call). This paper considers the provision of reflective practice to members of the SATU team, with a particular emphasis on their emotional and psychological experience at work. The introduction of reflective practice into a nursing setting will be discussed including opportunities and challenges that emerged, and how the service gained momentum over a year. The paper will reflect on one case example in the form of a supervisee/supervisor relationship in an effort to deepen and broaden our understanding of the need for professional spaces in which to consider ones experience at work.
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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 | 0.062 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.065 |
| Scholarly communication | 0.024 | 0.016 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".