Resilience of families with complex needs whose children manifest behavioural and emotional problems
Bibliographic record
Abstract
Although there is extensive literature on the risks faced by families dealing with cumulative challenges, including the emotional and behavioural problems of the child, limited research exists on the potential resilience of these families. Additionally, there are a lot of ambiguities in family resilience research with respect to indicators of good outcomes while facing risks. Therefore, this study aimed to answer the following research questions: 1) What risks do family members recognise that they are facing?; and 2) How do family members perceive good outcomes in the context of the risks that they are facing? To answer these research questions, we conducted group interviews with 8 families whose children, aged 12 to 18 years, manifest emotional and/or behavioural problems. Data were analysed using thematic analysis. For the first question, the results highlight one theme - Families with complex needs: multiple risks at different levels. Four themes contribute to answering the second research question: Survival, Not giving up and asking for help, Positive change, and Wish for togetherness and good communication. The families reported three indicators of good family outcomes, while striving for the fourth (Wish for togetherness and good communication), suggesting that outcome indicators can be distributed on a “continuum”. However, family members also reported that as they cope with and resolve the challenges they face, new risks emerge that may take the family back to an earlier stage, emphasising the circular rather than linear nature of this continuum. All participating families stressed that togetherness can be achieved with more professional help and time. The findings of this study address the importance of strength-based approaches in practice that will provide a space for fostering resilience in families facing chronic and cumulative risks.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".