Bibliographic record
Abstract
Dear readers, I am pleased to introduce the 2023 Issue of the International Journal of Child and Adolescent Resilience (IJCAR). This issue includes: two regular articles on trauma and maltreatment and associated impacts; three brief reports on varying aspects of hardships and resilience in different populations; and one theoretical paper about a narrative shelter model to give youth who experienced violence a voice and choice. In particular, the regular articles shed light on hardships and resilience experienced by differing populations. Bégin and colleagues examined the effects of using the internet survey method compared to a population-based telephone survey method on the profile of women with a history of child sexual abuse, in terms of socio-demographic data, victimization experiences, and mental health symptoms in adulthood. Jamison and colleagues aimed to gain insights on youth of color in the United States who have experienced violence regarding the associations between resilience and psychological health, social support, and school engagement. Furthermore, the 2023 issue of IJCAR also includes three brief reports on different topics associated to experiences of trauma, maltreatment, and resilience in various populations. Firstly, Brend et al. compared a sample of Finnish social workers serving children and families or social workers in other domains, in terms of the associations between moral distress and burnout amongst these groups. Secondly, Frederickson et al. investigated whether child maltreatment predicted the severity of posttraumatic stress disorder symptoms and symptom clusters for Canadian women during pregnancy, as well as changes in symptoms from pregnancy to three months postpartum. Finally, Souza et al. aimed to determine if social media in Brazil is a viable outlet for the dissemination of empirically supported information surrounding the topics of child abuse, adverse childhood events, and resilience. Finally, the issue ends with a theoretical paper by Nyirinkwaya and Jenney, regarding a shelter narrative model for research and practice on childhood experiences of intimate partner violence in young people, in order to integrate storytelling and storylistening to help youth exercise their voice and choice. Thus, we encourage you to read these various articles to obtain rich information about ongoing research in the field of resilience. I wish to also take this opportunity to sincerely thank all the members of the IJCAR team for their continuous and dedicated work in the editing and publishing process. Particularly, I wish to thank our associate editors, Dr. Tara Black, Dr. Delphine Collin-Vézina, Dr. Isabelle Daigneault, Dr. Rachel Langevin, and Dr. Nicole Racine; layout editor, Manon Robichaud; and managing editor and senior copyeditor, Teresa Pirro, all of which have done a fantastic job in their varying roles. I also want to take this opportunity to welcome Dr. Roxanne Guyon as a new associate editor. We hope you enjoy reading the current issue! This is also a friendly reminder to prepare your manuscripts for the next issue. Please submit your manuscripts in English, or in French, and feel free to forward this information to colleagues and students who may be interested. We look forward to your manuscripts and to the next issue of IJCAR. Happy reading! Martine Hébert, Editor-in-Chief
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.375 | 0.276 |
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".