AI Synthesis: Qualitative Analysis of Coach Learning

This post explains how to convert the PLoS One scoping review into extractable qualitative evidence using AI-enabled methods for researchers and coach developers. The primary keyword is qualitative analysis of coach learning. The PLoS One scoping review (Tan et al., PLoS One, 2026) mapped 11 studies and identified four qualitative topics: learning enablers, learning challenges, outcomes, and factors influencing change. Researchers and practitioners will get a step-by-step payoff: concrete statistics from the review, direct quotes to reuse in reports, and an AI workflow for thematic, frequency, and cross-segment analysis that scales small qualitative samples into robust program recommendations.

Key Takeaways

The PLoS One scoping review (Tan et al., PLoS One) shows that youth participation coach learning is shaped by access, facilitator quality, contextual fit, and follow-up, and AI-enabled qualitative analysis can make those patterns reproducible and actionable for coach developers. PLoS One.

The PLoS One review (Tan et al., published 18 August 2026) screened 3, 224 records from six databases up to 28 April 2025 and included 11 empirical studies for analysis.
According to Tan et al., 54.5% of included studies were conducted in Europe and 45.5% in North America, with 81.8% focusing on team sports and 63.3% specifically on football (data reported in 2026).
The PLoS One review found that 90.9% of the studies examined coach education programs and that 72.7% of those programs were small-scale and blended or online in delivery (Tan et al., 2026).
Direct participant quotes in Tan et al., PLoS One (2026) illustrate reflective change, for example: "Thinking back, I just feel coaching stuﬀ that I had seen others do and kind of thought why do I need to do anything different, " (Tan et al., PLoS One, 2026).
Use AI-enabled qualitative tools to systematize quotes, map enablers vs challenges, and test which program features (e.g., follow-up, contextual content) predict practice change in small samples.

What happened: PLoS One scoping review and core methods

What happened: The PLoS One scoping review (Tan et al., PLoS One, published 18 August 2026) mapped empirical literature on youth participation coach education and learning published January 2010 to 28 April 2025 and identified 11 studies for synthesis.

The PLoS One authors searched six databases (SPORTDiscus, ERIC, Scopus, Web of Science, APA PsycINFO, Medline) and imported 3, 224 references into Covidence for screening, removing 1, 337 duplicates, screening 1, 887 titles/abstracts, reviewing 82 full texts, and selecting 11 studies for analysis (Tan et al., 2026).

The PLoS One review combined descriptive statistics and qualitative topic analysis following Arksey and O’Malley’s framework, and it identified four qualitative topics: learning enablers, learning challenges, outcomes, and factors influencing change in coaches’ practice (Tan et al., 2026).

Constraints reported by the PLoS One review include geographic bias (all 11 studies in western countries), sport-type bias (nine of 11 focused on team sports), and small or mixed samples limiting generalizability (Tan et al., 2026).

Findings Snapshot

Date	Metric	Value	Implication
28 Apr 2025	Databases searched	6 databases; 3, 224 records identified	Comprehensive search but English-only scope may bias geography
Screening results (reported 2026)	Records screened / Full texts / Included	1, 887 screened; 82 full-text; 11 included	Narrow final sample means qualitative depth over breadth
Publication (Tan et al., 2026)	Geographic distribution	Europe 54.5%; North America 45.5%	Underrepresentation of non-western contexts
Included studies (Tan et al., 2026)	Sport & mode	Team sports 81.8%; Football 63.3%; CEP delivery blended/online 90.9%	Findings most applicable to team-sport CEP and digital/hybrid delivery
Program characteristics (Tan et al., 2026)	CEP duration	Less than 1 week 45.5%; 1–12 weeks 18.2%; >12 weeks 27.3%	Duration variety suggests need to test optimal follow-up and spacing

Implications for researchers and coach developers

Implication: Researchers should prioritize larger, more diverse samples and cross-cultural work because the PLoS One review (Tan et al., 2026) found all included studies came from western countries and only 11 studies met inclusion criteria.

Implication: Coach developers should design CEPs with contextual relevance and built-in follow-up because Tan et al., PLoS One (2026) reported coaches repeatedly cited short course duration and lack of follow-up as barriers to applying learning.

Implication: Program evaluators should combine qualitative reflection data with repeated behavioural observations because Tan et al., PLoS One (2026) noted mixed evidence from longitudinal mixed-methods studies when validating behaviour change.

How Evidano helps: AI workflows mapped to coach learning needs

Problem: Small, diverse qualitative datasets are hard to synthesize

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

Evidano feature mapping: ingest interview transcripts, coach reflections, and CEP materials, then run thematic clustering and frequency analysis to surface common enablers and barriers reported across a small sample.

Why it matters: The PLoS One review (Tan et al., 2026) included only 11 studies and highlighted small samples; Evidano can scale synthesis across small datasets to produce reproducible evidence for funders and clubs.

Problem: Tracking change in coach practice needs mixed data (text + observation)

Solution: Use Evidano to combine open-text survey responses, uploaded observation notes, and periodic interview transcripts to produce cross-segment analyses that link coach attributes to reported behaviour change.

Relevant Evidano tools: thematic, content, frequency, and cross-segment analyses plus AI chat over documents to rapidly iterate hypotheses with stakeholders (see features).

Why it matters: Tan et al., PLoS One (2026) recommended more longitudinal mixed-methods work; Evidano speeds triangulation of qualitative and coded observation data so teams can test which CEP design features predict uptake.

Problem: CEP designers need contextual content and structured follow-up

Solution: Evidence-based curriculum design with Evidano: tag coach quotes that request "practical, age-appropriate examples" or "follow-up meetings, " then export prioritized recommendations for revised modules and follow-up schedules.

Why it matters: PLoS One (Tan et al., 2026) found coaches valued contextual fit and post-course reflection; Evidano accelerates turning those qualitative signals into implementation plans.

Problem: Teams need secure, repeatable workflows for coach research

Solution: Evidano supports encrypted data handling, PII redaction for transcripts, and reproducible analysis scripts so organizations can report impact without exposing participant data.

Why it matters: Robust ethics and reproducibility practices complement the PLoS One review's call for higher-quality, generalizable research (Tan et al., 2026).

FAQ: qualitative analysis of coach learning

How can AI speed qualitative analysis of coach learning?

Answer: AI can rapidly code transcripts, cluster themes, and surface cross-segment patterns so researchers spend less time on manual coding and more on interpretation.

Supporting detail: The PLoS One review (Tan et al., 2026) identified reflective quotes and small samples as core evidence; AI-assisted thematic analysis preserves those quotations while quantifying their prevalence across cases.

What data should researchers collect to study coach education impact?

Answer: Collect pre/post interviews, open-text surveys, observation notes, and program artifacts to triangulate reported learning with observed practice.

Supporting detail: Tan et al., PLoS One (2026) showed mixed results in longitudinal mixed-methods studies, so combining qualitative reflections with systematic observations improves inference about behaviour change.

Can AI prove that a coach education program changed practice?

Answer: AI cannot by itself prove causation, but it can measure patterns and co-occurrence of reported learning and observed behaviours that strengthen causal claims.

Supporting detail: The PLoS One authors (Tan et al., 2026) reported mixed longitudinal evidence; AI-enabled cross-segment and timeline analyses help researchers document consistent temporal patterns supportive of program impact.

Is Evidano secure and suitable for coach research with PII?

Answer: Yes, Evidano supports encrypted storage and PII redaction to meet common research ethics requirements.

Supporting detail: For teams planning longitudinal coach studies like those recommended by Tan et al., PLoS One (2026), Evidano’s transcript redaction and secure workflows enable ethical data reuse and reproducible analysis (see data security).

Conclusion & Next Steps

The PLoS One scoping review (Tan et al., PLoS One, 2026) gives a focused map: access, coach developers, contextual fit, and follow-up matter for youth participation coach learning, but more diverse and larger studies are needed.

AI-enabled qualitative analysis turns the review’s small-sample insights into reproducible, stakeholder-ready recommendations by extracting themes, counting prevalence, and linking quotes to practice change.

If your team is designing CEPs or planning a mixed-methods evaluation, use AI workflows to accelerate synthesis and produce evidence that funders and clubs can act on.

Start with a pilot: collect 10–30 coach reflections, upload transcripts, and run a thematic + cross-segment analysis to prioritize curriculum changes. Try Evidano for free.

Topics

qualitative analysis of coach learning
coach education qualitative analysis
AI-enabled qualitative research

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