Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. The primary keyword for this post is qualitative analysis of coach learning, aimed at coach educators, sport researchers, and program evaluators who need fast, reproducible synthesis. According to the PLOS One scoping review by Tan et al. (published 18 August 2026), the youth participation coach learning literature included only 11 empirical studies drawn from an original search of 3, 224 records up to April 28, 2025. The PLOS One review found consistent themes about learning enablers, learning challenges, outcomes, and factors that influence whether coaches change their practice. This post shows how AI-enabled qualitative research can reproduce and scale that kind of synthesis, preserve direct participant quotes, and produce the summary statistics and topic maps that decision makers need.
Key Takeaways
The PLOS One scoping review mapped 11 studies on youth participation coach learning and found four topics (learning enablers, learning challenges, outcomes, and factors shaping practice) based on a literature search up to April 28, 2025 (PLOS One).
- 3224 records were returned in the database search and screened, of which 1337 were duplicates and 11 studies met inclusion criteria (search completed April 28, 2025), according to Tan et al. (2026).
- In the 11 studies mapped by the review, 9 studies (81.8%) focused on team sports and 7 studies (63.3%) specifically investigated football, as reported in PLOS One on 18 August 2026.
- The PLOS One review found that 10 of 11 studies (90.9%) examined coach education programs often delivered online or in blended formats and that coaches valued accessibility, reflection, social learning, and coach developers’ support (Tan et al., 2026).
- Common learning challenges recorded by the review included short course duration and lack of follow-up; 5 of 11 studies (45.5%) flagged brevity of courses as a barrier to applying learning (Tan et al., 2026).
What happened: how the PLOS One scoping review was done
The PLOS One scoping review searched six databases up to April 28, 2025 and followed Arksey and O’Malley’s five-stage framework and PRISMA-ScR reporting, as stated in Tan et al. (2026).
The PLOS One review imported 3, 224 records into Covidence, removed 1, 337 duplicates, screened 1, 887 titles/abstracts, reviewed 82 full texts, and included 11 empirical studies for analysis (Tan et al., 2026).
The PLOS One review reported descriptive statistics and a Braun and Clarke informed qualitative synthesis that produced four topic groups: learning enablers, learning challenges, outcomes, and factors influencing change in coaches’ practice (Tan et al., 2026).
Direct participant voice was preserved in the PLOS One review: for example a coach quoted in Tan et al. (2026) said, "I really enjoyed the freedom to go and search and learn about things that I wanted to learn about without being told I had to learn certain things."
Findings Snapshot
| Date / Source | Metric | Value (from PLOS One) | Implication for coach education research |
|---|---|---|---|
| Search completed April 28, 2025 (PLOS One) | Database hits | 3, 224 records | Large initial pool, but narrow final evidence base; systematic screening needed |
| Screening results reported in Tan et al. (2026) | Duplicates removed | 1, 337 records | Duplicate-heavy returns reinforce need for deduplication workflows |
| Screening results reported in Tan et al. (2026) | Full texts screened | 82 articles | Qualitative triage required to identify context-specific coach education studies |
| Final inclusion (Tan et al., 2026) | Included studies | 11 studies | Evidence is limited and skewed toward western team sports (research gap) |
| Content analysis (Tan et al., 2026) | Team sport focus | 9 of 11 studies (81.8%) | Underrepresentation of individual sports suggests need for diversified sampling |
| CEP delivery modes (Tan et al., 2026) | Online / blended CEPs | Blended or online in most studies; 18.2% fully online, 72.7% blended | Digital delivery is common; opportunities to evaluate specific digital tools |
| Course duration (Tan et al., 2026) | Program lengths | Less than 1 week in 5 studies (45.5%); >12 weeks in 3 studies (27.3%) | Wide variation in duration points to uncertainty about optimal learning timelines |
Implications for coach educators and sport researchers
Coach educators should prioritize accessible, context-relevant, and follow-up-rich programs because Tan et al. (2026) found coaches value accessibility, reflection, and ongoing support across 7 of the 11 studies.
Researchers should diversify sampling and contexts because the PLOS One review (published 18 August 2026) found all 11 studies were from western countries and 9 studies (81.8%) focused on team sports, leaving non-western and individual sports underexplored.
Program evaluators should measure both coach-level outcomes and downstream athlete outcomes because Tan et al. (2026) reported that only a minority of studies linked coach learning to athlete outcomes such as enjoyment or engagement.
How Evidano helps: AI solutions for qualitative analysis of coach learning
Problem: Small, dispersed qualitative datasets slow synthesis
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Solution: Evidano ingests transcripts, workshop notes, and CEP materials and produces reproducible thematic maps and frequency counts so you can move from raw text to topic summaries in hours instead of weeks.
Relevant feature: automated thematic, content frequency, and cross-segment analyses (Features).
Problem: Manual coding loses participant voice and slows traceability
Solution: Evidano preserves verbatim quotes, links them to coded themes, and exports code→quote matrices for auditability, matching the PLOS One review’s emphasis on direct participant quotes (Tan et al., 2026).
Relevant feature: exportable codebooks and quote matrices for reporting and publication (Features).
Problem: Audio and hybrid CEPs need fast transcription and redaction
Solution: Evidano’s transcription pipeline converts workshop and interview audio to text with custom dictionaries and PII redaction so data are research-ready for analysis.
Relevant feature: speech-to-text and transcription tooling (Speech-to-text).
Problem: Stakeholders want clear, evidence-backed recommendations
Solution: Evidano generates extractable summaries, tables of descriptive statistics, and visualization exports so coach educators can show program impact and gaps similar to the PLOS One descriptive mapping approach.
Relevant feature: AI chat over documents and visual exports for stakeholder-ready deliverables (Features).
Data governance and reproducibility
Evidano supports encrypted data storage and reproducible analysis logs so your qualitative pipeline is auditable for publication and stakeholder review.
If you want a template to reproduce Tan et al.’s approach, Evidano can ingest the included studies and produce the same code clusters, frequency tables, and quote extracts used in the PLOS One synthesis.
FAQ: qualitative analysis of coach learning
What is qualitative analysis of coach learning and why does it matter?
Answer: Qualitative analysis of coach learning identifies themes, enablers, barriers, and outcomes from coach interviews, observations, and program materials.
Supporting detail: The PLOS One scoping review used thematic coding and topic summaries to surface four primary topics across 11 studies, learning enablers, learning challenges, outcomes, and factors influencing change (Tan et al., 2026).
How can AI speed thematic synthesis of coach education research?
Answer: AI accelerates coding, clustering, and extraction of representative quotes while keeping coding decisions exportable and auditable.
Supporting detail: The PLOS One review processed 3, 224 records to 11 included studies (search up to April 28, 2025); AI can drastically reduce time spent on reading and first-pass coding across large pools of documents.
Can AI preserve participant quotes and ethical redaction?
Answer: Yes, AI platforms can both preserve verbatim quotes and apply PII redaction when required.
Supporting detail: The PLOS One review highlighted the value of verbatim coach quotes; Evidano’s transcription and PII redaction options let researchers retain voice while meeting ethics obligations.
How should coach-education evaluations measure impact on practice?
Answer: Combine qualitative reflection and social learning evidence with pre/post behavioural observation and, where possible, athlete outcome measures.
Supporting detail: Tan et al. (2026) noted mixed longitudinal evidence and recommended more studies that link coach learning to practice change and athlete outcomes.
Conclusion & Next Steps
The PLOS One scoping review by Tan et al. (published 18 August 2026) shows a limited but consistent evidence base about how youth participation coaches learn and what supports change in their practice.
AI-enabled qualitative research reduces the manual bottlenecks in that kind of synthesis, preserves participant voice, and produces the descriptive tables and topic maps decision makers need to design better CEPs.
If you run CEP evaluations or coach learning research, you can reproduce the PLOS One descriptive mapping and go further with automated thematic analysis and exportable codebooks.
To try an AI-first qualitative workflow on your coach interviews or CEP materials, Try Evidano for free.
Topics
- qualitative analysis of coach learning
- coach education qualitative analysis
- AI qualitative research for coaching
- coach learning thematic analysis
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