An exploration into the prevalence and experience of neurodiversity among staff at a UK high‐secure psychiatric hospital
Bibliographic record
Abstract
BACKGROUND: Whilst there is increasing recognition and interest in how staff who have neurodivergence are represented and contribute to industry, to date, there has been very little exploration of neurodivergence among healthcare staff and none with those working in forensic psychiatric services. AIMS: To estimate the prevalence of neurodivergence among staff working in a UK high-secure psychiatric hospital and explore their experiences. METHODS: All staff were invited to complete a confidential online self-report questionnaire consisting of both closed and open questions. RESULTS: A fifth of the staff responded (n = 124), but two did not report their neurodivergent status, so were excluded. Nearly 40% of responders reported having some form of neurodivergence, with Attention Deficit Hyperactivity Disorder (ADHD) being the most common, followed by dyslexia, autism and dyspraxia. Over a third (36%) of those reporting neurodivergence said that they had chosen not to disclose this to the organisation and around a quarter (23%) reported not feeling valued or supported by the organisation. Of the staff cohort who responded, most expressed the view that neurodiversity in the workforce is beneficial to patient care, despite the majority being unsure whether neurodivergence is an advantage or a barrier for the individual themselves. Over half of staff completing the survey felt there was not enough awareness of neurodiversity among staff and that more training on the subject would be beneficial. CONCLUSION: This survey suggests gaps in our recognition and understanding of neurodivergence among hospital staff. Although the prevalence figure should be treated with caution, given the low response rate, in this first survey of neurodivergence among staff in a secure hospital, our findings suggest substantial numbers of staff with neurodivergence and the likelihood that neither they nor the organisation are optimising this situation. The further steps should include the development and evaluation of strategies exploring ways of improving recognition and relevant support for staff, as well as discovering ways in which neurodivergence in staff might inform and optimise care and treatment for patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".