The activation of resilience in placed children, as seen by child protection services professionals in Ontario, Canada
Bibliographic record
Abstract
Although resilience has been scientifically studied from a number of different angles, its activation, with the help of significant professional assistance, is of particular interest in the search for innovative intervention strategies, specifically for children exposed to abuse. Based on the practical knowledge of fifty social workers in Ontario’s child welfare services, this study documents in detail the activation of the resilience process in children who have experienced abuse, and who are considered by participants to have used their full resilience potential. The interdisciplinary position taken here leads us to open up several fields of investigation, as well as to bring together a range of disciplinary knowledge, using empirical data and theoretical materials. Neuroscientific breakthroughs, for example, help us to gain a better understanding of the subject matter. Our analysis of accounts reveals that the mechanism by which the process of resilience is activated is really set in motion in interventions that emphasize the support, assistance, help, and the use of resources—the circumstance favorable for the activation of resilience. In this study, we break new ground and propose the concept of “activated resilience,” which we define as follows: A situation in which individuals, with the help of a resilience mentor, can grasp the present and overcome their fears, appropriate their sense of existence, and find their place within society.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".