Exploring the transfer of the ‘Hotspots’ system to tackle bullying, harassment and discrimination
Bibliographic record
Abstract
Background: Bullying, discrimination and harassment (BDH) at work is a significant global issue with health, economic and social consequences. Health trainees on placements are known to experience BDH, but few solutions exist. We developed an innovative, award-winning* integrated system (‘HOTSPOTS’) which successfully tackles workplace BDH experienced or witnessed by medical students. It uses a survey and associated processes to provide ‘safety in numbers’ for students and confidential, benchmarked reporting for clinical leaders (Chief Medical Officers and Department Heads), motivating action. A progress summary is reported back to students. HOTSPOTS is in place in half of New Zealand’s hospitals. *Safeguard New Zealand Workplace Health and Safety Awards https://www.auckland.ac.nz/en/news/2022/06/28/team-tackling-bullying-wins-national-award.html.). With parts of our sector moving toward requiring BDH metrics as part of programme accreditation in Canada and Australasia, now is the time to identify how to transfer HOTSPOTS. We are evaluating its implementation in New Zealand in order to understanding transfer challenges. We will present initial findings of an ongoing implementation fidelity study (funded by an Ember Wellbeing Trust Grant). This will include views from stakeholders (students, chief medical officers, and university staff) and measures of system performance (response rates, number of actions taken etc.) Objectives: To outline and share lessons from an award-winning BDH reporting/action system for medical students on clinical placements To explore whether this system could be useful elsewhere, and barriers or enablers in transferring it to other programmes, institutions, countries or sectors. Discussion: Alongside preliminary implementation fidelity findings, practicalities of running HOTSPOTS over the last 3.5 years will be discussed. This includes examples of issues identified by students, consequent actions taken, strengths and weaknesses of the HOTSPOTS system, and future plans. Exploration : Would a system like HOTSPOTS transfer to your workplace? If not, why not? What (if any) alternative are others using to measure and act on BDH?
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".