Bibliographic record
Abstract
BACKGROUND: As one of selft-help groups for the bereaved by cancer in my hospital ''KIRARA-KAI'' members wanted to learn the meaning of grief care, I and members of this group planned to hold four series of lectures on grief care for the bereaved, especially the bereaved by cancer, and medical staffs.This is the first trial in Shiga prefecture of Japan.The purpose of this study is to investigate the effectiveness of lectures on grief care.METHOD: Participants of lectures: 1st lecture N 5 73, 2nd lecture N 5 60 Participants of questionnaire: 1st lecture N 5 41,2nd lecture N 5 42 Lecturers: The bereaved and specialists on grief care (psychiatrist, palliative care doctor, clinical psychologist, nurse) Schedule: 1st and 2nd lectures were held 28/8/2010 and 13/11/2010 respectively.3rd one will be 30/4/2011, and 4th one will be sometime this year.Contents of lectures: grief process, the most useful support that the bereaved experienced, narrative of the bereaved, many kinds of grief care, the words that would hurt the bereaved, etc. Questionnaire: Participants answered impression and understanding on lectures and wrote free comments, etc. RESULTS: The result of Questionnaire: According to questionnaires on 1st and 2nd lectures, over 93% participants of questionnaires answered ''Very good ''and ''Good''.And over 88% participants of questionnaires answered ''easy to understood'' and ''understood''.Free comments of participants: The examples of comments are as follows: ''I have ever suffered from my grief, but now I can understand that my reaction is normal and I require my story listening for recovering.''''The lecture was concrete and I really understood the lecture on checking my experience.by spouse-caregivers, counselling and social support can reduce the distress they felt.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.744 | 0.397 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".