The Stages of Group Development: A Retrospective Study of Dynamic Team Processes
Bibliographic record
Abstract
Abstract The number of organizations using teamwork is increasing. The team phenomenon has heightened our need to better understand what makes these groups more or less effective. Unfortunately, methods of assessing dynamic team processes such as group development have been limited. The purpose of this study was to create a simpler quantitative method of measuring temporal changes in group processes. A retrospective questionnaire was developed to measure the constructs of Tuckman's stage development model. Both the reliability and content validity analyses provided evidence that the retrospective method can be used to evaluate group development stages. Résumé Le nombre d'organisations ayant recours au travail d'équipe est à la hausse. Le phénomène de l'«équipe » accroît la nécessité de mieux comprendre ce qui rend ces groupes plus ou mains performants. Malheureusement, les méthodes d'évaluation du type d'interactions au sein du groupe et de la dynamique qui en résulte, restent limitées. Le but de cette étude est de créer une méthode quantitative plus simple pour mesurer les changements temporels dans la dynamique de groupe. Un questionnaire rétrospectif a été élaboré pour mesurer les facteurs du modèle à phases de Tuckman. Les analyses relatives à la fiabilité et celles relatives à la validité, ont montré que la méthode rétrospective peut servir à évaluer les phases du développement du groupe.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".