Helping Families Affected by Depression: Incorporating Prosocial and Caregiving Literature
Bibliographic record
Abstract
In this study, we use prosocial and caregiving literature to strengthen the family section of anti-depression campaigns. We suggest that the addition of the prosocial and caregiving insights bring a new perspective on depression caregiving of family members that goes beyond a mere description of the family’s reactions and coping abilities. Built on essential components such as empathy, knowledge and skills instead of fear or anxiety, the development of a specific theoretical framework for designers of anti-depression campaigns targeting people caring for family members suffering from depression increases our understanding of family member behavior as caregivers. It provides families with a comprehensive tool that includes motivational and determining factors in one’s will and ability to deliver appropriate help in the face of family illness. Specific recommendations for designers of social marketing campaigns are provided. In addition, we exemplify how an anti-depression campaign targeting family members of depressed people is consistent with the prosocial and caregiving literature, while waiting for a formal evaluation of the effectiveness of the model presented in this article.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".