Evaluation of an educational program for the caregivers of persons diagnosed with a malignant glioma
Bibliographic record
Abstract
BACKGROUND: Caring for a loved one with a malignant glioma can be a formidable responsibility. The guarded prognosis, side effects of treatments, and changes in brain function, personality and behaviour pose unique challenges in care provision by family members. It is rare that institutions provide educational programs for caregivers. PURPOSE: To evaluate the impact of providing information in an educational program to caregivers of patients diagnosed with a malignant glioma. METHODS: A structured educational program for caregivers of brain tumour patients was developed based upon multidisciplinary expert opinion and caregiver feedback. Twenty-four caregiver participants were enrolled in the program. Knowledge was assessed before, immediately following, and four to six weeks following the program. Open-ended questions were used to explore the caregivers' experiences, as well as additional benefits derived from the program. RESULTS: Knowledge scores on testing immediately after the program and four to six weeks following the program were statistically significantly improved from baseline testing, although there was a decline in scores four to six weeks after the program. These findings demonstrate effective knowledge transfer (recall of the information) immediately after the education program and four to six weeks later. Specific qualitative and quantitative data serve as a basis for understanding caregivers' needs and experiences.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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, 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".