Playing Up Qualitative Analysis: Youth Camogie

Playing up an age grade raises tradeoffs between accelerated skill development and higher injury or burnout risk for adolescent athletes; coaches and researchers need clear, evidence-based syntheses to decide who benefits and why. This post explains the key findings from a player‑voice study of youth Camogie through the lens of qualitative analysis methods and AI-enabled workflows, and it is written for sport researchers, youth coaches, and qualitative teams who analyse interview data. The primary keyword for this post is playing up qualitative analysis, and the practical payoff is a concise set of extractable insights you can reuse in coaching decisions or research coding schemes. The post also shows how AI-assisted transcription and thematic analysis speed synthesis while preserving participant voice and trustworthiness.

Key Takeaways

According to the PLOS ONE study, playing up an age grade produced mostly positive developmental outcomes for youth Camogie players when supported, alongside identifiable risks (PLOS ONE).

Sample and timing: the PLOS ONE study interviewed n = 15 players (mean age 14.4 ± 1.1 years) in interviews conducted between May 1 and August 31, 2024.
Prevalence and context: the PLOS ONE study reports that 80% of participants (12 of 15) played up only at club level, and 67% (10 of 15) had played up in prior years.
Balance of effects: the PLOS ONE study found 80% of participants expressed favourable perceptions of playing up (reported in the study published on August 11, 2026), citing skill gains and psychosocial benefits but also reporting pressures, injury concern, and social friction.
Player voice: participants framed playing up as both opportunity and challenge, for example P7 said “if I wanted to, I could, ” and P11 said “you get used to all that stuff quick enough.”
Practical implication: the PLOS ONE study recommends individualised, autonomy‑supportive decisions and coach strategies (team induction, load management, phased exposure) to maximise benefits and reduce risks.

What happened and how the study measured perceptions

Answer: the PLOS ONE study used semi-structured interviews and reflexive thematic analysis to capture youth players' lived experience of playing up an age grade.

According to the PLOS ONE study, researchers recruited 15 adolescent Camogie players and conducted online Zoom interviews between May 1 and August 31, 2024, with all interviews audio-recorded and transcribed for analysis.

According to the PLOS ONE study, participant characteristics included a mean age of 14.4 ± 1.1 years and an average 7.2 ± 2.2 years of playing experience, and transcripts were analysed using a six-step reflexive thematic analysis protocol described in Braun and Clarke.

According to the PLOS ONE study, trustworthiness steps included researcher reflexivity, dual‑author coding as “critical friends, ” adherence to the Standards for Reporting Qualitative Research, and availability of the data on the Open Science Framework.

Findings Snapshot

Date / Period	Metric	Value	Implication
May–Aug 2024	Interviews conducted	n = 15 adolescent players	Primary qualitative dataset used for thematic synthesis
May–Aug 2024	Mean age	14.4 ± 1.1 years	Dataset focuses on mid-adolescence developmental stage
Sample reported in study (published Aug 11, 2026)	Played up at club level	80% (12 of 15)	Most playing-up experiences occurred in club contexts
Sample reported in study (published Aug 11, 2026)	Previously played up	67% (10 of 15)	Majority had repeated exposures to higher-grade competition
Background statistic cited by authors	Camogie dropout	15% dropout rate reported in 2023 study (for Camogie)	Playing-up decisions should consider retention risk

Implications for coaches and qualitative researchers

What should coaches change when offering players to play up?

Answer: coaches should individualise playing-up decisions and use autonomy-supportive onboarding and load management, according to the PLOS ONE study.

According to the PLOS ONE study, players recommended structured team-bonding, patient coaching, phased integration into training, and explicit listening to the younger player's preferences to reduce social friction.

According to the PLOS ONE study, coaches should assess emotional regulation, self-efficacy, and social readiness in addition to physical maturity before promoting a player.

What should qualitative researchers note when studying 'playing up' phenomena?

Answer: researchers should prioritise participant voice, reflexivity, and transparent coding to capture nuance, according to the PLOS ONE study.

According to the PLOS ONE study, semi-structured interviews, reflexive thematic analysis, and dual-author iterative coding increased credibility; researchers should report sample characteristics (e.g., n, mean age, prior exposures) and make data available when possible.

According to the PLOS ONE study, including contextual metrics (club vs county, rural vs urban, prior playing-up history) improves transferability and supports subgroup analysis.

How Evidano helps with playing up qualitative analysis

Problem: interview transcription and error-prone manual coding

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

According to the PLOS ONE study, interviews were audio-recorded and automatically transcribed; using AI transcription with a custom dictionary and PII redaction streamlines this step and preserves participant quotes like “an honour in our club” (P10).

Solution: use Evidano's transcription features to produce accurate transcripts and export timestamps, reducing manual error and accelerating reflexive coding workflows. See the Evidano speech-to-text features for relevant capabilities.

Problem: synthesising themes across small samples and comparing subgroups

Answer: mapping codes to frequency and cross-segment analyses reveals patterns that support coach decision-making, according to the PLOS ONE study which emphasises subgroup context.

Solution: Evidano's thematic, content, frequency, and cross-segment analyses let researchers quantify how many participants raised a theme (for example pressure, social exclusion, or competence gains) while preserving exemplar quotes for reporting. See Evidano features for coding and visualization tools.

Problem: maintaining data security and participant trust

Answer: research teams should avoid exposing sensitive interview data to third-party training datasets, a concern for athlete voice research noted in qualitative methodology guidance cited by the PLOS ONE study.

Solution: Evidano encrypts data and does not use uploaded data to train third-party models, supporting ethics and data‑sharing practices while enabling collaborative coding across teams. Learn more on Evidano data security.

FAQ: playing up qualitative analysis

What does the PLOS ONE study say are the main benefits of playing up?

Answer: the PLOS ONE study reports improved skill development, decision-making, and psychosocial competence as primary benefits.

According to the PLOS ONE study, players described accelerated technical gains, stronger physicality on return to their age group, and social mentoring from older teammates as core benefits that increased confidence and readiness for the next level.

What are the main risks of playing up identified in the study?

Answer: the PLOS ONE study identifies increased injury risk, psychosocial pressure, and reduced intrinsic motivation as principal risks.

According to the PLOS ONE study, participants reported heightened expectations, schedule clashes across teams, and anxiety about performance; the authors caution about overtraining and recommend individualised readiness assessments.

How many players and what age range did the study use?

Answer: the PLOS ONE study interviewed 15 players with mean age 14.4 ± 1.1 years.

According to the PLOS ONE study, the 15 participants had 7.2 ± 2.2 years of Camogie experience and interviews took place online between May 1 and August 31, 2024.

Can AI help reproduce the study's reflexive thematic analysis?

Answer: yes, AI-augmented tools can speed coding and pattern detection but should be combined with human reflexivity to mirror the study's approach.

According to qualitative best practice and the PLOS ONE study methods, AI can assist with initial code generation and frequency counts, while human researchers perform iterative theme development, critical friend review, and contextual interpretation.

Conclusion & Next Steps

According to the PLOS ONE study, playing up an age grade can foster accelerated competence and psychosocial growth when decisions are individualised and supported by coaches; however, the same study reports measurable concerns about pressure, injury risk, and social integration that require mitigation.

Researchers and coaches can reuse the study's concrete metrics (n = 15; mean age 14.4 ± 1.1; interviews May–Aug 2024) and exemplar quotes to design evaluation checklists and phased exposure protocols.

If you run interview-based research or coach education projects and want to scale transcription, extract thematic frequencies, and generate cross-segment reporting while preserving participant voice, try an AI workflow.

Get started with an AI-enabled qualitative platform and manage your projects securely: Try Evidano for free.

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qualitative analysis of playing up
youth sport playing up
AI qualitative research

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Playing Up Qualitative Analysis: Youth Camogie

Key Takeaways

What happened and how the study measured perceptions

Findings Snapshot

Implications for coaches and qualitative researchers

What should coaches change when offering players to play up?

What should qualitative researchers note when studying 'playing up' phenomena?

How Evidano helps with playing up qualitative analysis

Problem: interview transcription and error-prone manual coding

Problem: synthesising themes across small samples and comparing subgroups

Problem: maintaining data security and participant trust

FAQ: playing up qualitative analysis

What does the PLOS ONE study say are the main benefits of playing up?

What are the main risks of playing up identified in the study?

How many players and what age range did the study use?

Can AI help reproduce the study's reflexive thematic analysis?

Conclusion & Next Steps

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