HPOP Results: AI-enabled Qualitative Analysis

The primary finding is that the 2021 Northern Territory Institute of Sport High Performance Officiating Program produced measurable psychosocial gains for several participants, and AI-enabled qualitative analysis can accelerate mixed-methods program evaluation. According to the PLOS One study, three of the five officials showed reliable improvements in self-efficacy and resilience from baseline to program completion in November 2021, and the paper reports specific percentage changes and Reliable Change Indices for individual participants. Program evaluators and sport psychologists evaluating small, multimodal interventions should expect idiographic patterns rather than uniform group averages, according to the PLOS One study published August 5, 2026. This post explains the study findings, reproduces key numbers and quotes from the source, and then shows concrete ways AI-enabled qualitative research workflows reduce coding time, surface mechanism-level themes, and link those themes to individual quantitative trajectories.

Key Takeaways

According to the PLOS One article published August 5, 2026, the 2021 NTIS High Performance Officiating Program (HPOP) produced reliable improvements in self-efficacy and resilience for three of five participating officials, with repeated measures collected in February, May, August, and November 2021.

Sample and timing: the study tracked five officials in the 2021 HPOP cohort across four timepoints in February, May, August, and November 2021, according to PLOS One.
Magnitude: the PLOS One study reports a 37.5% increase in resilience for two officials (Jamie and Alex) from baseline to time 4, with Jamie’s RCI = 3.99 and Alex’s RCI = 3.99, as reported in the article.
Individual results: the PLOS One article documents mentor-rated self-efficacy increases as large as 67.57% for one official (Alex) from baseline to time 4, with RCI = 7.20.
Mixed-methods insight: the PLOS One study combined idiographic quantitative trajectories with thematic analysis of open responses to link increased opportunity, mentoring, and social support to higher self-efficacy, according to PLOS One.

What happened in the NTIS HPOP (what, who, when, how measured)

Answer: the 2021 NTIS High Performance Officiating Program delivered workshops, mentoring, and psychological skills sessions to a five-person cohort and measured psychosocial outcomes at four timepoints in 2021.

According to the PLOS One article, six applicants were selected for the NTIS HPOP intake in 2021 and five completed the study, with data collection occurring at baseline (February 2021), two midpoints (May and August 2021), and program completion (November 2021).

According to the PLOS One article, the study used validated instruments: the CD-RISC10 for resilience, the Referee Self-Efficacy Scale (REFS) for self-efficacy subscales (game knowledge, decision making, pressure, communication), the Cognitive Appraisal Scale for challenge and threat, Rotter’s Locus of Control, and the Functional Assertiveness Scale.

According to the PLOS One article, the analytic approach combined idiographic repeated-measures (percent change, mean change, and Reliable Change Index) with an inductive thematic analysis of open questions, enabling the authors to link individual quantitative change to participants’ words and mentors’ observations.

Snapshot table: key numeric findings from the PLOS One analysis

Date / Metric	Value reported in PLOS One	Implication for evaluators
August 5, 2026 / Publication	PLOS One article publishes evaluation of 2021 NTIS HPOP	Peer-reviewed documentation available for replication and secondary analysis
2021 / Sample size	Five officials completed the program (data at four timepoints)	Small N requires idiographic methods and careful interpretation
Baseline to Nov 2021 / Resilience (Jamie)	37.5% increase, RCI = 3.99 (PLOS One)	Large individual effect, unlikely due to measurement error
Baseline to Nov 2021 / Mentor-rated self-efficacy (Alex)	67.57% increase, RCI = 7.20 (PLOS One)	Substantial mentor-observed gains; triangulation supports validity
Feb–Nov 2021 / Measurement design	Four repeated measures: Feb, May, Aug, Nov 2021 (PLOS One)	Allows trajectory analysis and detection of non-linear change

Implications for program evaluators and sport psychologists

Answer: evaluators should plan for idiographic mixed-methods when samples are small and interventions are multimodal, because the PLOS One study shows heterogeneous individual trajectories.

According to the PLOS One article, three of five officials showed reliable improvements in self-efficacy and resilience from baseline to completion (November 2021), which implies that group averages would have masked important individual change patterns.

According to the PLOS One article, mentors’ ratings sometimes diverged from self-ratings early and converged over time, which implies evaluators should collect both participant and observer perspectives to detect alignment and potential mentor effects.

According to the PLOS One article, qualitative themes (resilience, technical skills, pre/post routines, adaptive responses, social support) mapped to quantitative gains, which implies that linking coded themes to participant-level trajectories helps identify mechanisms of change.

How Evidano helps: accelerate the mixed-methods workflow used in the PLOS One HPOP study

Problem: small N plus multimodal data slows synthesis

Answer: researchers with small cohorts and multiple measurement types spend weeks aligning quotes, workshop notes, and repeated measures.

According to typical program evaluations like the PLOS One HPOP study, aligning four timepoint questionnaires with open-ended responses and mentor notes is time consuming and error prone.

Solution: Evidano for AI-enabled qualitative analysis

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

Evidano maps the PLOS One workflow by ingesting transcripts and survey spreadsheets, auto-coding open answers with transparent theme labels, and linking those codes to participant IDs and timepoints for cross-segment analysis.

According to Evidano users, AI-assisted thematic clustering and automatic co-occurrence networks reduce manual coding time and make idiographic pattern detection (participant-level trajectories) faster and auditable.

Feature match (problem → Evidano feature)

Problem: manual thematic coding across timepoints → Feature: automated inductive theme generation with reviewer oversight (transparency and audit trail). See features.

Problem: linking qualitative themes to numeric RCIs and percent change → Feature: cross-segment and timepoint analytics that join survey spreadsheets to coded text.

Problem: transcription and remote workshops in 2021 were hybrid → Feature: built-in transcription and PII redaction to ingest recorded sessions reliably, see speech-to-text.

How this shortens an HPOP-style evaluation

Answer: Evidano reduces time to synthesis from weeks to days for small-cohort mixed-methods studies by automating initial coding and linking themes to numeric trends.

Evidano supports idiographic reporting by exporting participant-level code timelines and facilitating reviewer reconciliation, which matches the PLOS One study’s idiographic emphasis and helps produce tables like those in the publication with less manual work.

FAQ: ai-enabled qualitative analysis

How many participants can AI-enabled qualitative analysis handle for an idiographic program evaluation?

Answer: AI-enabled qualitative analysis handles both small idiographic samples and larger cohorts; it scales the same coding logic across cases.

According to the PLOS One example, the HPOP study used an idiographic design with five officials and benefited from linking individual trajectories to themes, showing that AI workflows are valuable even for N = 5.

Can AI reproduce the thematic analysis method used in the PLOS One HPOP study?

Answer: Yes, AI can reproduce an inductive six-stage thematic pipeline but should be supervised by domain experts.

According to the PLOS One article, the authors used an inductive thematic analysis and independent coder checks; AI tools can generate initial codes and themes and then allow human reviewers to confirm and refine them.

What safeguards are needed when using AI for qualitative research?

Answer: Use transparent codebooks, reviewer reconciliation, and secure data handling.

According to best-practice mixed-methods guidance and platform documentation, the evaluator should keep an audit trail of AI suggestions and human edits and confirm that any platform used does not leak data to third-party model training.

How do I link quotes to numeric change like RCIs or percent change?

Answer: Tag each quote with participant ID and timepoint, then join those tags to your spreadsheet of numeric scores.

According to the PLOS One example, linking the February–November 2021 timepoint tags to RCIs and percent-change calculations allowed the authors to map phrases such as “I feel more confident in my abilities” to measurable resilience and self-efficacy gains.

Conclusion & Next Steps

Answer: the PLOS One evaluation of the 2021 NTIS HPOP shows meaningful, individual-level psychosocial gains and demonstrates why AI-enabled qualitative analysis is practical for mixed-methods program evaluation.

According to the PLOS One article published August 5, 2026, the study combined idiographic repeated-measures and thematic analysis to show reliable increases in resilience and self-efficacy for several participants and to identify mechanisms such as mentoring and social support.

If you run small-cohort or multimodal evaluations and want to automate coding, link quotes to numerical trajectories, and produce auditable idiographic reports, consider Evidence-based AI workflows.

Get started by exploring our features or try a hands-on trial. Try Evidano for free.

Topics

ai-enabled qualitative analysis
AI qualitative analysis
qualitative program evaluation
HPOP evaluation
sports officials resilience

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