Qualitative analysis of sexual violence disclosures

Sexual violence disclosures on Canadian campuses are common but mostly hidden: the BMJ Open protocol (published 1 August 2025) notes >90% of incidents are not disclosed to institutions and 71% of students report witnessing or experiencing unwanted sexualised behaviours. This scoping review protocol maps how disclosure is defined, studied and experienced in Canadian postsecondary settings and plans reflexive thematic analysis in autumn 2025 (search started March 2025). If you run qualitative research or shape campus policy, this post shows how to turn that messy evidence base into reliable themes, segment comparisons and stakeholder-ready visuals using AI-enabled qualitative research. See the original protocol at www.bmjopen.bmj.com/content/15/8/e099628 and learn how to operationalise the findings in Evidano (www.evidano.com) without exposing sensitive data to third‑party LLMs.

Fast take, why this protocol matters

This BMJ Open scoping review protocol (published 1 August 2025) asks: “what are the perceptions and experiences of disclosure processes of sexual violence in Canadian postsecondary institutions? ” The authors will review peer‑reviewed and grey evidence from 1 December 2014 onward, use Arksey & O’Malley’s five‑step framework and apply Braun & Clarke’s reflexive thematic analysis. Read the protocol at www.bmjopen.bmj.com/content/15/8/e099628.

Core payoff: synthesize how disclosure is defined, who’s heard, and where gaps create harm for equity‑deserving students.
Practical value: policymakers, campus researchers and student‑services teams get a codebookable evidence map to improve trauma‑informed supports.

Findings snapshot

Date / Metric	Value	Source / Note
Published	1 August 2025	BMJ Open scoping review protocol
Non‑disclosure rate	>90%	Protocol cites Canadian postsecondary estimates
Students reporting unwanted behaviours	71%	Protocol summary (Canadian studies)
Inclusion window	From 1 Dec 2014 onward	Captures post‑2016 policy changes (e.g., Ontario Bill 132)
Search start / Analysis planned	Search started March 2025; analysis scheduled autumn 2025	Protocol methods

What the protocol does (methods in plain English)

This is a scoping review protocol, designed to map the breadth of evidence rather than appraise study quality. The team will search PsycINFO, ERIC, Sociological Abstracts, Criminal Justice Abstracts and Scopus, include English sources from 1 December 2014, and intentionally capture grey literature (policy docs, reports) relevant to Canadian postsecondary settings. Screening is two‑stage (title/abstract, full text) with two independent reviewers and Covidence for workflow management.

Analysis plan: reflexive thematic analysis following Braun & Clarke (six steps) with an intersectional lens to highlight equity gaps.
Key distinctions: the review separates ‘disclosure’ (sharing an experience) from formal ‘reporting’ (initiating institutional processes).
Limitations flagged by authors: excludes non‑English sources, no formal quality appraisal (typical for scoping reviews).

So what for researchers, UX teams and policy analysts

For qualitative researchers

Use the protocol’s codeframe approach to build reproducible codebooks: start with the authors’ planned fields (definitions of disclosure, theoretical lens, populations represented).

Document where evidence excludes equity‑deserving groups (Indigenous, international, 2SLGBTQ+, students with disabilities) and prioritize purposive sampling or outreach where gaps exist.

For campus policy & student‑services teams

Distinguish mandatory reporting from supportive practice. The protocol highlights evidence that broad mandatory reporting can deter help‑seeking, translate findings into ‘support first’ procedures.

Prioritize culturally safe and trauma‑informed pathways for groups with historically low disclosure rates.

For UX / service designers

Map disclosure touchpoints (peers, faculty, counselling, security) and test micro‑interactions that reduce friction and preserve autonomy.

Design intake language and forms informed by themes (trust, fear of retaliation, cultural safety) the review will surface.

Do more, faster with Evidano (mapped to this use case)

Ingest and standardize diverse evidence

Problem: protocol combines peer‑reviewed papers, policy documents and grey reports. Solution: Evidano ingests PDFs, Word files and spreadsheets and normalizes metadata so you can code across source types.

Rapid thematic extraction and codebook alignment

Problem: manual thematic coding of many documents is slow and inconsistent. Solution: run AI‑assisted thematic analysis seeded with Braun & Clarke steps, export a hierarchical codebook (themes→subthemes) and iteratively refine with human‑in‑the‑loop review.

Cross‑segment comparisons and visuals

Problem: identifying how disclosure experiences differ by group (e.g., Indigenous vs international students) is tedious. Solution: Evidano’s cross‑segment frequency and co‑occurrence analyses surface which themes cluster by cohort and produce shareable visuals (co‑occurrence networks, word clouds, hierarchical code maps).

Secure & compliant handling of sensitive text

Problem: working with survivor accounts raises privacy and security concerns. Solution: Evidano offers PII redaction, encryption at rest/in transit and a policy: customer data is never used to train third‑party models.

From finding to stakeholder-ready outputs

Export thematic summaries, coded excerpts, and slide‑ready visuals for policy briefs or university boards, cut synthesis time from weeks to days.

Practical 7‑step workflow to reproduce the protocol’s synthesis in Evidano

Follow these steps to move from raw sources to an evidence map you can act on:

1) Gather sources: PDFs of studies, policy docs, reports and relevant survey spreadsheets (inclusion window from 1 Dec 2014 per the protocol).
2) Upload to Evidano; apply PII redaction and, if needed, custom dictionary for campus‑specific terms.
3) Run automatic extraction: metadata, speaker labels, and initial topic clustering.
4) Seed an AI‑assisted thematic analysis (use Braun & Clarke prompts); iterate codebook with two reviewers to mirror the protocol’s double‑screen approach.
5) Run cross‑segment frequency and co‑occurrence analyses (e.g., theme prevalence by student group).
6) Generate visuals and export quote decks for stakeholder review; tag quotes by safety/citation needs.
7) Export reproducible reports and raw coded data for transparency, archiving and policy drafting.

FAQ: common questions about applying AI to sensitive qualitative data

Is this appropriate for trauma‑related research?

Yes, use AI to surface patterns and speed synthesis, but pair automated outputs with human clinical/trauma expertise. This work is research‑focused and non‑diagnostic; ensure ethics and survivor protections when working with firsthand accounts.

How do you compare segments reliably?

Use Evidano’s cross‑segment filters (metadata tags like student status, identity, institution) and run frequency + co‑occurrence metrics; then validate differences with manual spot checks.

How secure is the platform?

Evidano encrypts data in transit and at rest and does not use customer data to train third‑party LLMs, important when handling disclosures and sensitive personal narratives.

Wrapping up, next steps

The BMJ Open scoping protocol (www.bmjopen.bmj.com/content/15/8/e099628) will produce an evidence map that campus teams can use to redesign disclosure pathways. If you’re preparing to synthesise similar literature or operationalize findings into policy or service design, try the workflow above in Evidano to accelerate coding, compare equity groups, and produce stakeholder‑ready visuals without compromising security.

Ready to test this on your corpus? Start a pilot: upload 10–20 documents and run a thematic + cross‑segment analysis in days, not weeks.
Schedule a demo or start a trial at www.evidano.com to see how the protocol’s methods map to a reproducible, secure AI‑enabled qualitative workflow.

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