This post refracts the PLOS One scoping review through the lens of AI-enabled qualitative research and is aimed at coach educators, sport researchers, and learning designers who need faster, more reliable synthesis of coach learning evidence. The primary keyword "qualitative analysis of coach learning" guides practical recommendations and reproducible workflows drawing on the PLOS One scoping review (Tan et al., 2026). According to the PLOS One study, the authors screened 3, 224 records (search conducted to April 28, 2025) and included 11 empirical studies, producing a compact but information-rich corpus ideal for AI-assisted thematic analysis.
Key Takeaways
The PLOS One scoping review (Tan et al., 2026) mapped 11 studies on youth participation coach learning and identified four qualitative topics: learning enablers, learning challenges, outcomes, and factors influencing change in practice. PLOS One
- The PLOS One review searched six databases up to April 28, 2025 and returned 3, 224 records, of which 1, 337 were duplicates and 11 studies were included for analysis (Tan et al., PLOS One, published August 18, 2026).
- Geographic and topic skew: 6 of 11 studies (54.5%) were European and 9 of 11 (81.8%) focused on team sports; football appeared in 7 of 11 studies (63.3%) according to Tan et al. (PLOS One, 2026).
- Program design signals: 10 of 11 studies (90.9%) examined coach education programs, 8 of 11 (72.7%) used blended delivery, and 7 of 11 (63.6%) used mixed methods, indicating common use of small, online or hybrid CEPs (Tan et al., PLOS One, 2026).
What happened and how the review was conducted
What happened: The authors conducted a scoping review to map research on youth participation coach learning using Arksey and O'Malley’s five-stage framework and searched SPORTDiscus, ERIC, Scopus, Web of Science, APA PsycINFO, and Medline up to April 28, 2025 (Tan et al., PLOS One, 2026).
How the data were measured and selected: According to Tan et al. (PLOS One, 2026), 3, 224 initial records were imported into Covidence, 1, 337 duplicates were removed, 1, 887 titles/abstracts were screened, 82 full texts were assessed, and 11 articles were included for charting and qualitative synthesis.
Constraints: The review limited inclusion to English peer-reviewed articles published from January 2010 to April 28, 2025, which the authors note likely biased the sample toward Euro-North American contexts (Tan et al., PLOS One, 2026).
Findings snapshot
| Date / Source | Metric | Value | Implication |
|---|---|---|---|
| Search cutoff: April 28, 2025 (Tan et al., PLOS One) | Records returned | 3, 224 | Feasible corpus size for mixed manual + AI coding workflows |
| Screening outcome (Tan et al., PLOS One; published Aug 18, 2026) | Studies included | 11 | Narrow but in-depth evidence base, ripe for pooled qualitative synthesis |
| Geography (Tan et al., PLOS One) | Western countries | 11 of 11 studies (100%) in Europe or North America; 6 Europe (54.5%), 5 North America (45.5%) | Recommend targeted studies in non-western contexts |
| Study focus (Tan et al., PLOS One) | Team sports and football | 9 team sport studies (81.8%); 7 football (63.3%) | Caution when generalizing to individual sports |
| CEP format (Tan et al., PLOS One) | Delivery mode | 8 blended (72.7%); 2 fully online (18.2%) | Online/hybrid design is common and acceptable to coaches |
Implications for coach educators and sport researchers
Short answer: Coach educators should design CEPs that are contextually relevant, flexible, and include follow-up support, because the PLOS One review found short course length and lack of follow-up were recurring challenges (Tan et al., PLOS One, 2026).
Research priorities: According to Tan et al. (PLOS One, 2026), future studies should expand to non-western contexts, individual sports, and larger, more diverse samples to improve generalizability (recommendations published August 18, 2026).
Practical design choices: The PLOS One review reported that coaches valued accessibility, reflection, social learning, and skilled coach developers, suggesting CEPs should include asynchronous resources, facilitated peer reflection, and periodic follow-up touchpoints (Tan et al., PLOS One, 2026).
How Evidano helps research teams turn coach learning literature into actionable insights
What problem does AI address in qualitative syntheses of coach learning?
Answer: Small, heterogeneous literatures like the 11-study corpus in PLOS One require systematic mapping, rapid thematic coding, and cross-study frequency analysis to reveal patterns and gaps.
Supporting detail: According to Tan et al. (PLOS One, 2026), the included studies vary by method, sport, and CEP format, which makes manual synthesis slow and prone to missed cross-study themes.
Evidano definition and data protections
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Supporting detail: Evidano ingests documents and transcripts, produces thematic, frequency, and cross-segment analyses, and supports encrypted storage and private LLMs so your data are not used to train third-party models.
Problem: manual coding is slow and inconsistent → Feature: AI-assisted thematic coding
Answer: Evidano accelerates code generation and harmonizes labels across studies to summarize themes like the four topics in Tan et al. (PLOS One, 2026): learning enablers, learning challenges, outcomes, and change factors.
Supporting detail: For a corpus of 11 papers, an AI-assisted pipeline can extract participant quotes, cluster codes (e.g., "accessibility, " "reflection"), and compute occurrence counts that match the descriptive statistics reported in the PLOS One review.
Problem: mixed-methods data integration → Feature: cross-segment analysis and visualization
Answer: Evidano links qualitative codes to metadata (study location, sport, CEP duration) so researchers can answer questions such as whether blended CEPs correlate with reported behavior change in coaches.
Supporting detail: The PLOS One review reported 8 blended CEPs and 7 mixed-methods designs (Tan et al., PLOS One, 2026); Evidano can compute co-occurrence networks and show which study attributes align with desired outcomes.
Problem: extracting verbatim quotes and citations → Feature: searchable transcript and source linking
Answer: Evidano indexes source documents and preserves verbatim quotes with source attribution so reviewers can produce tables of representative quotes like the coach reflections quoted in Tan et al. (PLOS One, 2026).
Supporting detail: The PLOS One paper includes participant quotes such as “Thinking back, I just feel coaching stuff that I had seen others do…” which AI indexing can surface alongside the original study citation.
Try it with your coach education corpus
Answer: To pilot the workflow, upload the 11 PLOS One articles or your CEP evaluation transcripts into Evidano, run automated coding, and refine themes with human-in-the-loop review.
Supporting detail: Learn more about relevant features on the Evidano Features page before you upload your documents.
FAQ: qualitative analysis of coach learning
How can AI speed up qualitative synthesis of coach education studies?
Answer: AI speeds synthesis by auto-extracting themes, clustering similar quotes, and producing frequency counts that highlight recurring concepts across studies.
Supporting detail: For example, the PLOS One review identified repeated topics (e.g., accessibility, reflection); an AI pipeline can replicate that mapping across 3, 224 initial records and the 11 included studies to surface recurring patterns faster (Tan et al., PLOS One, 2026).
Which statistics from the PLOS One review are most useful to extract automatically?
Answer: Key extractable statistics include counts of studies by geography, sport type, CEP format, and study method because they reveal sampling bias and design gaps.
Supporting detail: The PLOS One review reports 6 European studies (54.5%), 9 team-sport studies (81.8%), and 8 blended CEPs (72.7%), all of which are measurable metadata fields for automated dashboards (Tan et al., PLOS One, 2026).
Can AI preserve the original authors' quotations and attribution?
Answer: Yes, AI indexing preserves verbatim quotes and links them to the original document and page, enabling transparent citation.
Supporting detail: The PLOS One paper includes coach quotes like “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” which should be presented with exact attribution in any synthesis (Tan et al., PLOS One, 2026).
Is AI suitable for small corpora like the 11 studies in the PLOS One review?
Answer: Yes, AI is well suited to small corpora because it speeds coding and surfaces cross-study links that manual review may overlook.
Supporting detail: The PLOS One authors used manual charting and thematic analysis; an AI-assisted approach complements that work by providing reproducible codebooks and by enabling rapid re-analysis if new studies are added.
What ethical safeguards should researchers use when applying AI to coach learning data?
Answer: Researchers should ensure source attribution, participant privacy, secure storage, and human oversight of automated codes.
Supporting detail: The PLOS One review focused on published peer-reviewed studies; when working with interview transcripts or participant data, platforms should support encryption and PII redaction and not share data with third-party model trainers.
Conclusion & Next Steps
Recap: The PLOS One scoping review (Tan et al., 2026) distilled a small but instructive literature (11 studies, searched to April 28, 2025) that emphasizes accessible, context-relevant CEPs and the need for follow-up support to translate learning into practice.
Next steps for researchers: Use AI-assisted qualitative tools to harmonize codes, count cross-study patterns, and extract verbatim quotes with source links for rapid evidence summaries.
If you want to pilot an AI workflow on coach education literature or your CEP evaluation transcripts, start a free trial and Try Evidano for free to upload documents, run thematic and cross-segment analyses, and generate citation-ready extracts.
Topics
- qualitative analysis of coach learning
- AI qualitative analysis coach education
- youth coach learning research
- coach education qualitative methods
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