Transforming Social Regularities in a Multicomponent Community‐Based Intervention: A Case Study of Professionals’ Adaptability to Better Support Parents to Meet Their Children's Needs
Bibliographic record
Abstract
This paper presents an in-depth case study of the dynamic processes of mutual adjustment that occurred between two professional teams participating in a multicomponent community-based intervention (CBI). Drawing on the concept of social regularities, we focus on patterns of social interaction within and across the two microsystems involved in delivering the intervention. Two research strategies, narrative analysis and structural network analysis, were used to reveal the social regularities linking the two microsystems. Results document strategies and actions undertaken by the professionals responsible for the intervention to modify intersetting social regularities to deal with a problem situation that arose during the course of one intervention cycle. The results illustrate how key social regularities were modified in order to resolve the problem situation and allow the intervention to continue to function smoothly. We propose that these changes represent a transition to a new state of the ecological intervention system. This transformation appeared to be the result of certain key intervening mechanisms: changing key role relationships, boundary spanning, and synergy. The transformation also appeared to be linked to positive setting-level and individual-level outcomes: confidence of key team members, joint planning, decision-making and intervention activities, and the achievement of desired intervention objectives.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".