Time-Window Sequential Analysis: An Introduction for Pediatric Psychologists
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Pediatric psychologists are often interested in interactions among individuals (e.g., doctors and patients, parents and children). Most research examining the nature of these interactions has used correlational analyses. Sequential analysis provides greater detail on contingencies during interactions and the way that interactions play out over time. The purpose of this article is to offer a non-technical introduction to sequential analyses for pediatric psychologists. METHODS: A more recent derivation of the basic method, called time-window sequential analysis, is introduced and distinguished from other forms of sequential analysis. RESULTS: A step-by-step pediatric psychology example of time-window sequential analysis is provided and the integration of sequential analysis with traditional statistical methods is discussed. An example of physician-child interaction during anesthesia induction is used to illustrate the technique. CONCLUSION: Sequential analysis is a technique that is useful to pediatric psychologists who are interested in contingencies among data collected over time.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it