31 Determinants: Qualitative Analysis of SEN Research

The PLOS One study published on November 13, 2026, used two stakeholder workshops in Northern Ireland to develop an implementation research logic model for physical activity in SEN schools. The study identified 31 implementation determinants, eight practical strategies, eight causal mechanisms, and 24 outcomes across implementation, school, and pupil levels.

• According to the November 2026 PLOS One study, 16 participants from six special schools and related organisations contributed to both workshops.
• The November 2026 PLOS One study identified 18 facilitators, 12 barriers, and one factor that functioned as both a barrier and a facilitator.
• The November 2026 PLOS One study grouped the determinants into five CFIR 2.0 domains: innovation, outer setting, inner setting, individual characteristics, and process.
• The November 2026 PLOS One study translated the findings into eight implementation strategies, including staff training, stakeholder networks, family engagement, and adaptation to context.
• The authors concluded in November 2026 that the implementation research logic model should be treated as a flexible framework that local stakeholders can adapt, rather than as a prescriptive model.

The study found that successful whole-school physical activity implementation in SEN settings depends on context, stakeholder collaboration, staff capability, resources, and adaptation to pupils’ diverse needs. Byrd and colleagues conducted two four-hour, in-person workshops in Northern Ireland, with recruitment completed before the first workshop on November 29, 2024.

The 16 workshop participants included two parents, four teachers, one classroom assistant, two school leaders, five Education Authority Northern Ireland representatives, and two community or voluntary-sector professionals, according to the PLOS One study published on November 13, 2026. The researchers recruited participants with lived or professional experience in special education and used a collaborative co-development approach informed by community-based participatory research.

The research team recorded and transcribed the workshops, then used deductive content analysis. According to the study methods, two researchers coded transcripts and related materials against CFIR 2.0, ERIC, the Implementation Outcomes Framework, Normalization Process Theory, COM-B, and the implementation research logic model, with disagreements resolved through discussion.

The authors stated, "We have co-developed an IRLM embedded in implementation and behavioural science, " describing the model as a way to connect determinants, strategies, mechanisms, and outcomes. The study also emphasised that the frameworks worked best as complementary and flexible guides shaped by stakeholder experience.

Qualitative researchers should preserve links between stakeholder statements, contextual determinants, implementation strategies, and outcomes when analysing SEN implementation data. The PLOS One study demonstrates why a simple list of themes is not enough for complex school interventions: decision-makers also need to understand how a proposed strategy is expected to produce change.

The study’s first implication is that stakeholder diversity improves implementation analysis. Parents, teachers, classroom assistants, school leaders, education authorities, and community organisations may describe different barriers, so a cross-segment comparison can reveal where perspectives align or conflict.

The study’s second implication is that coding frameworks should support, not replace, participant experience. Byrd and colleagues reported that some stakeholder priorities did not fit neatly into predefined constructs, which required an iterative analysis process and contextual adaptation.

The study’s third implication is that outcome analysis should distinguish implementation outcomes from school-level and pupil-level outcomes. The PLOS One researchers noted that reach, acceptability, sustainability, staff training, stakeholder engagement, pupil enjoyment, and participation answer different evaluation questions.

For education and health researchers, AI qualitative analysis can accelerate transcript review, code grouping, frequency counts, and comparisons across stakeholder segments. Human researchers must still validate interpretations, protect participant privacy, and avoid treating mention frequency as proof of importance. The PLOS One study explicitly cautioned that outcome frequencies indicated prominence in workshop discussions, not relative importance.

The November 2026 PLOS One study shows how rigorous qualitative analysis can turn stakeholder workshops into an implementation research logic model for physical activity in SEN schools. Its 31 determinants, eight strategies, eight mechanisms, and 24 outcomes provide a structured example for researchers studying complex interventions.

AI qualitative analysis can make this workflow faster by helping teams organise transcripts, compare stakeholder segments, identify recurring patterns, and connect evidence to implementation questions. Human review remains essential, especially when research involves children, disability, health, or sensitive educational information.

Researchers who want to analyse interviews, workshops, surveys, and documents in one secure workflow can Try Evidano for free.