EXPERIENCES OF EXPERTS INVOLVED IN THE IMPLEMENTATION OF THE EXTENDED PROFESSIONAL PROCEDURE FOR CHILDREN WITH PROBLEMS IN BEHAVIOR
Bibliographic record
Abstract
The extended professional procedure (EPP) is carried out at primary schools as a special form of preventive work with children who manifest problems in their behavior and their primary environment. This paper aimed to investigate the experiences of experts — educators with backgrounds in social work, social pedagogy, or psychology, involved in the implementation of the EPP. We considered three research questions to investigate: (1) behavioral problems of children who participate in the EPP, (2) educators’ experiences with the implementation of the EPP, and (3) educators’ ideas for improving the program. For data collection, a qualitative focus group method was used, which included experts who have implemented the EPP; for data analysis, thematic qualitative analysis was used. The results show that children who were included in the EPP often manifested milder types of risky behaviors and usually came from families burdened with difficulties, a known risk factor for behavioral problems. Experiences of educators with the implementation of the EPP were both positive and negative. The negative aspects were mainly related to cooperation with the pedagogical service, social work institute, and parents, while the positive aspects were mainly related to the children’s perception of the program and the experts. The experts’ proposals and recommendations included improving the program by developing a clear definition of the EPP in both theory and practice, then paying strict attention to that definition.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 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".