Do problem‐solving skills help mitigate emotional distress through perceived control and self‐efficacy in parents of children with cancer?
Bibliographic record
Abstract
Abstract Introduction Parents of children with cancer face psychological challenges that can result in significant distress. It has been found that problem‐solving (PS) could mitigate emotional distress (ED) in this population, but mechanisms of this relation are poorly understood. This study aimed to assess whether there is a link between PS and ED through perceived control and self‐efficacy. Methods We included 119 parents (67 mothers, 52 fathers, including 50 couples) whose child was diagnosed with cancer. We evaluated whether PS was associated with ED through perceived control and self‐efficacy in couples of parents. Results We found no direct association between PS and ED (β = −0.01, p = 0.92). Our results indicated a significant indirect effect between ED and PS with perceived control as the intermediary variable (β = −0.24, p < 0.001, 95% CI [−0.41, −0.11]). However, there was no indirect association between ED and PS with self‐efficacy as the intermediary variable (β = −0.04, p = 0.26, 95% CI [−0.11, 0.09]). The effect size was large in magnitude (R2 = 0.59 for ED). Conclusion The mitigating role of PS on ED is better explained by an enhanced experience of control than by improved self‐efficacy. Future interventions should directly target the action mechanism behind PS and ED in both mothers and fathers by targeting their perceived control.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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, 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".