AI for Qualitative Analysis of Moral Injury

Solution: Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents, per Evidano.

Evidano supports encrypted storage and controlled access, which aligns with the PLOS One article’s ethical restriction on public transcript release due to re-identification risk, according to the article (Data Availability).

Evidano feature mapping: use automated transcription with PII redaction and a custom dictionary to preserve Arabic religious terms like Muraqib or Rahmah while protecting identity, see Evidano speech-to-text.

Solution: Evidano provides AI-assisted thematic coding, code hierarchies, and cross-segment frequency analysis to accelerate IPA-style patterning while preserving idiographic excerpts.

Evidano feature mapping: exportable, auditable code maps and code→subcode hierarchies let you document the six themes the PLOS One article identified and show which career-stage segments expressed each theme, consistent with transparency practices described in the article (Methods).

Evidano integrates secure team annotations and member-checking workflows so researchers can reproduce the audit trail that the PLOS One study used to demonstrate dependability.

Solution: Evidano’s translation tools let you supply glosses and a custom dictionary so Arabic terms retain transliteration and interpretive notes as the PLOS One article preserved culturally specific terms during analysis, per the Methods section.

Evidano feature mapping: use the translation feature to keep term-level fidelity and record forward/back-translation notes for auditability.

Solution: Evidano generates exportable tables, quote collections, and frequency matrices so you can present the N = 25 sample statistics and verbatim quotations such as “I’m betraying them” (Fatimah) exactly as the PLOS One article does in its Results.

Evidano feature mapping: combine quote pullouts, co-occurrence networks, and exportable CSVs to support policy proposals like ethical-impact assessments and protected teacher voice forums described in the PLOS One article (Discussion).

For product details see Evidano features and our security commitments at Evidano data security.

Direct answer: Interpretative Phenomenological Analysis is appropriate for studying moral injury in education when the research goal is idiographic depth and sense-making, according to the PLOS One article (Methods).

Support: The PLOS One study used IPA with N = 25 in order to foreground teachers’ lived meanings and to develop an ecological model linking macrosystem policy to individual internalization.

Direct answer: Small purposive samples can produce robust themes; the PLOS One article gathered 25 in-depth interviews and reported six super-ordinate themes.

Support: The PLOS One study explains that 25 interviews were chosen to balance idiographic depth and cross-case patterning and that 32 teachers were initially approached with 7 excluded for incompleteness.

Direct answer: Report participant counts, recruitment attrition, interview length, and approval dates; the PLOS One article provides all of these details (Received 14 Feb 2026; Accepted 31 Jul 2026; Published 14 Aug 2026).

Support: The PLOS One study lists 32 approached, 7 excluded, final N = 25, and interview lengths of 60–90 minutes as core reproducibility metrics.

Direct answer: Use short verbatim quotes with named attributions and contextual metadata; the PLOS One article includes quotes such as “It’s more through the experienced pressures and routines we go through rather than the policy documents we actually read sometimes” (Maha).

Support: The PLOS One study embeds participant quotes next to thematic interpretation to preserve voice and to support claims about institutional translation and classroom collision.

Direct answer: Yes, if the AI tool supports custom dictionaries, bilingual checks, and exportable glosses, as used by the PLOS One study during translation checks described in Methods.

Support: The PLOS One article reports forward translation, independent back-translation of 20 percent of transcripts, and iterative resolution of culturally specific terms, a workflow you can operationalize with Evidano translation features.