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Record W7065635664

Exploring the transfer of the ‘Hotspots’ system to tackle bullying, harassment and discrimination

2023· other· en· W7065635664 on OpenAlexaboutno aff

Bibliographic record

VenueResearchSpace (University of Auckland) · 2023
Typeother
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsnot available
Fundersnot available
KeywordsHarassmentAccreditationFidelityWork (physics)Order (exchange)
DOInot available

Abstract

fetched live from OpenAlex

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?

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.088
GPT teacher head0.292
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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