Increasing Sexual Violence Reporting and Disclosure in Higher Education Institutions: A Proposed Approach to Critically Analyze the Internal Organizational Context
Bibliographic record
Abstract
AIMS: This article explores the underreporting of sexual violence (SV) in higher education, highlighting serious implications for survivors who may silently cope with its aftermath instead of accessing crucial resources. METHODS: We utilize Bolman and Deal's four-frame model for organizational change to assess how internal factors within organizations may influence reporting of SV. The four frames-symbolic, structural, human resources, and political-offer a systematic analysis of the internal organizational context in higher education institutions concerning SV reporting. RESULTS: Our suggested approach offers concrete dimensions and probing questions for examination. Derived from a qualitative study, our recommendations align with Bolman and Deal's four-frame model, aiding in assessing the organizational environment. This approach assists stakeholders in identifying barriers/facilitators in the internal organizational context of higher education institutions, enabling effective planning for improved SV reporting/disclosure. CONCLUSIONS: A thorough analysis is essential for understanding factors influencing campus SV reporting. Our proposed critical analysis and recommendations serve as a starting point to identify organizational barriers/facilitators, informing the revision of SV policies and processes, including reporting. POTENTIAL IMPACT OF IMPROVED SEXUAL ASSAULT REPORTING IN HIGHER EDUCATION INSTITUTIONS ON FORENSIC NURSING AND SURVIVORS/VICTIMS ALLIES: Enhanced reporting of sexual assault in higher education benefits forensic nurses and allies, like student affairs, advocacy groups, unions, SV coordinators, health centers, equity departments, human rights officers, and administration. Improved analysis of institutional and cultural contexts allows for tailored services to better meet survivors' needs. Increased reporting should lead institutions to higher service utilization, requiring careful planning for resource allocation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".