Agir au mieux pour prévenir et contrer la maltraitance envers les enfants du Québec
Bibliographic record
Abstract
Family violence against children is a major social issue in Quebec. For optimal results, societal response must take place upstream, through universal preventive interventions, as well as downstream, through reactive interventions with families requiring protection services. Three promising programs subject to effectiveness evaluations were described: Espace, strategy for universal prevention of sexual, physical and verbal abuse; SIPPE (Integrated Perinatal and Early Childhood Services), abuse and neglect prevention strategy for families living in a context of vulnerability; and PAPFC2 (new generation of personal, family and community aid program), intervention involving multiple strategies aimed at families at high-risk for child neglect. Some findings relating to the effectiveness of the programs were then discussed. Better established in prevention than in protection, the assessment of the robustness of data related to effectiveness is relatively mixed, and even the most solid findings must not be taken for granted. Moreover, programs whose effectiveness has still not been demonstrated should not be ignored since today’s innovative programs may be tomorrow’s exemplary programs. The development of a culture of evaluation in practice settings and fruitful collaboration between researchers and workers suggest that knowledge of the effectiveness of the actions undertaken will be enhanced.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".