Identifying Family Members Who Are Likely to Perceive Benefits From Providing Care to a Person With a Primary Malignant Brain Tumor
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To identify changes in positive aspects of care (PAC) from the time of diagnosis to four months following the diagnosis in family caregivers of care recipients with primary malignant brain tumors. DESIGN: Longitudinal. SETTING: Dyads were recruited from neurosurgery clinics in Pittsburgh, PA, at the time of care recipients' diagnosis with a primary malignant brain tumor. A second data collection took place four months following the diagnosis. SAMPLE: 89 caregiver and care recipient dyads. METHODS: Paired t tests were used to examine change in PAC, univariate analyses were used to determine predictors of PAC at four months, Mann-Whitney U tests and t tests were used to examine associations between categorical predictor variables and PAC at four months, and univariate linear regressions were used to examine associations between continuous predictors and PAC at four months. MAIN RESEARCH VARIABLES: The impact of sociodemographic factors, caregiver-perceived social support, mastery, neuroticism, and marital satisfaction on PAC. FINDINGS: Caregivers' PAC scores during the first four months following diagnosis appeared to remain stable over time. Significant differences were found between the care recipient reasoning domain group at diagnosis and PAC score. Care recipients who scored below average were associated with caregivers with higher PAC scores. Caregiver PAC at four months following diagnosis was significantly predicted by care recipient reasoning and caregiver social support. CONCLUSIONS: PAC scores appear to remain stable over time, although levels of PAC may be related to care recipients' level of functioning. Future research should focus on the development of interventions for caregivers who report low levels of PAC at the time of diagnosis in an attempt to help these individuals identify PAC in their caregiving situation. IMPLICATIONS FOR NURSING: Findings have clinical and research implications. Clinicians may be able to better identify caregivers who are at risk for negative outcomes by understanding the risks faced by caregivers of patients with milder symptoms in addition to those caring for more profoundly affected care recipients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".