A Review of Behavioral Observation Coding Approaches for the Trier Social Stress Test for Children
Bibliographic record
Abstract
The Trier Social Stress Test (TSST) has become one of the most widely-used protocols for inducing moderate psychosocial stress in laboratory settings. Observational coding has been used to measure a range of behavioral responses to the TSST including performance, reactions to the task, and markers of stress induced by the task, with clear advantages given increased objectivity of observational measurement over self-report measures. The current review systematically examined all TSST and TSST-related studies with children and adolescents published since the original work of Kirschbaum et al. (1993) to identify behavioral observation coding approaches for the TSST. The search resulted in 29 published articles, dissertations, and master's theses with a wide range of coding approaches used. The take-home finding from the current review is that there is no standard way to code the Trier Social Stress Test for Children (TSST-C), which appears to stem from the uniqueness of investigators' research questions and sample demographics. This lack of standardization prohibits conclusive comparisons between studies and samples. We discuss relevant implications and offer suggestions for future research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".