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Record W4383560005 · doi:10.12968/bjhc.2022.0035

Barriers to reporting sexual harassment in healthcare settings: a literature review

2023· review· en· W4383560005 on OpenAlexaboutno aff
Tabitha Morgan Rees

Bibliographic record

VenueBritish Journal of Healthcare Management · 2023
Typereview
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsnot available
Fundersnot available
KeywordsHarassmentChecklistCLARITYHealth careScopusGrey literatureCritical appraisalPsychologyInclusion (mineral)Medical educationHealth professionalsMedicinePublic relationsNursingPolitical scienceMEDLINEAlternative medicineSocial psychology

Abstract

fetched live from OpenAlex

Healthcare professionals are at risk of experiencing sexual harassment at work, which can have a negative impact on their health and wellbeing. This study aimed to explore the reasons why healthcare professionals may not report their experiences of sexual harassment, as documented in peer-reviewed literature. EBSCO, Scopus, JSTOR, Science Direct, Sage, Google Scholar, the Cochrane Library and DelphiS were searched to identify relevant articles published in English from Australia, Canada, Ireland, New Zealand, UK and USA. A concept mapping table was developed to assist with the search strategy and the Critical Skills Appraisal Programme Checklist was used to critique and synthesise the findings. A total of five articles met the inclusion criteria, although most focused on healthcare professionals in general and looked at wider harassment and violence, rather than sexual harassment alone. The main factors that influenced the reporting of harassment were the normalisation of inappropriate behaviour, fear of reprisal and lack of clarity around policies and procedures. Further research is needed into the factors influencing the reporting of sexual harassment specifically in order to develop standardised reporting procedures and training programmes to prevent these incidents, and handle them appropriately when they do occur.

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)0.015
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.808
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.063
GPT teacher head0.430
Teacher spread0.367 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations2
Published2023
Admission routes1
Has abstractyes

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