Qualitative analysis of IPSPC in primary care

Evidano is an AI-powered qualitative data analysis platform that ingests documents and surveys, applies AI-assisted coding, and produces reproducible thematic and cross-segment analyses. This short guide describes a minimal, reproducible workflow to extract themes from the IPSPC evaluation (published 25 June 2026), map evidence to the programme Theory of Change, and compare participant segments to inform policy.

• The IPSPC evaluation, published 25 June 2026 by the Department for Work and Pensions, is an independent mixed-methods study that assesses delivery, outcomes and system effects of expanding IPS into primary care.
• Qualitative analysis should map interviews and surveys to the IPSPC Theory of Change, compare segments (referral route, condition group, employment status), and quantify theme frequencies to inform decisions.
• A seven-step reproducible workflow is provided, from ingest and ToC mapping to AI-assisted coding, cross-segment analysis and audit-ready synthesis.

The UK evaluation of Individual Placement and Support in Primary Care (IPSPC) is an independent mixed-methods study published on 25 June 2026 that assesses delivery, outcomes and system effects of widening IPS into primary care and adding job-retention support.

• Source: Department for Work and Pensions
• Scope: adults with mild-to-moderate physical or mental health conditions (Equality Act 2010 definition).

This table summarises publication date, programme scope, methods and the report URL for the IPSPC evaluation.

The evaluation combined participant and stakeholder interviews, provider and employer feedback, and participant surveys to test hypotheses derived from the IPSPC Theory of Change. It focused on delivery (what services did), reach (who engaged), and outcomes (job starts, retention, and perceived benefits). The IPSPC innovation is twofold: widening referrals to primary care and offering support to people at risk of losing existing jobs.

• Design: Mixed-methods, theory-driven; qualitative strand mapped to the ToC.
• Data sources: Interviews with stakeholders, providers, participants and employers, plus quantitative survey data from participants.
• Analytic aim: Explain what worked, for whom, why, and system implications for future policy.

Follow this seven-step workflow to reproduce the IPSPC qualitative analysis and move from report to decisions:

• 1) Gather inputs: upload the IPSPC report, interview transcripts, and survey CSVs to Evidano.
• 2) Map the Theory of Change: create ToC nodes and tag documents and quotes to hypotheses.
• 3) Import or create a codebook: standardise codes for barriers, enablers, employer response, and outcomes.
• 4) Run AI-assisted coding: apply codes across transcripts and extract exemplar quotes with source metadata.
• 5) Cross-segment analysis: compare themes by referral route, condition group, and employment status; generate co-occurrence networks.
• 6) Synthesize: produce an executive brief with top themes, supporting quotes, and quantified frequencies for each ToC hypothesis.
• 7) Iterate: use AI chat over the dataset to produce targeted follow-up questions or generate a short plan for piloting recommended changes.

The IPSPC evaluation (25 June 2026) offers a focused mixed-methods dataset that policy teams can translate into operational changes if analysis maps cleanly to the Theory of Change and segments are compared systematically.

• Next move: upload the report and related transcripts to Evidano to produce reproducible thematic and cross-segment analyses and stakeholder-ready visuals.
• Try a pilot: Try Evidano for free using the IPSPC materials or your programme data, secure and audit-ready.

Ethics note: This post is research-focused and non-diagnostic. Follow local consent and data-protection rules when handling interview and survey data.