Using the Critical Incident Technique to explore situational emotional labor in sport psychology: methodological proposals
Bibliographic record
Abstract
Interventions in sport settings involve intense emotional labor (i.e., effort to adjust emotional responses to professional expectations). This has mainly been explored using quantitative, person-centered approaches that identify general trends. Over the past decades, however, the Critical Incident Technique (CIT) has attracted increasing attention in psychological research as an effective innovative tool for examining emotional labor during crucial circumscribed situations. Following a presentation of the CITs origins and development across different sport fields (e.g., elite sport, physical education), the aim of this article is to put forward CIT-based methodological proposals for analyzing sport interveners' situational emotional labor. More precisely, we outline two primary data collection modes using questionnaires and interviews. Each approach offers distinct advantages according to the research objectives, context, and participant accessibility. The paper also includes guidelines for both qualitative and quantitative analysis of critical incidents by suggesting how data can be thematically coded and statistically analyzed. In terms of data representation, this article focuses on juxtaposing participant quotes within shared thematic categories and introduces "creative non-fiction" as an innovative narrative technique, presenting data as structured monologues that preserve the authenticity of the participants' own voices. Finally, the present article outlines future research directions, emphasizing the relevance of the CIT in observational and interventional studies aimed at better understanding and supporting emotional labor among sport interveners. In the case of observational studies, four main future research avenues can be explored by: (a) developing longitudinal observatories to enable long-term tracking of emotional labor; (b) adopting a collective approach to emotional labor by incorporating multiple actors' points of view during a same critical incident; (c) deepening understanding of the relationships between emotional experience and emotional regulation during critical incidents; and (d) validating a psychometric scale capable of assessing emotional labor during critical incidents across large samples. As for interventional studies, a training program designed to develop adaptive and flexible emotional regulation through CIT use is presented. This program would benefit from being implemented and evaluated, again using the CIT, to reveal its effects on participants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".