A Research Literature Review to Determine How Bereavement Programs Are Evaluated
Bibliographic record
Abstract
A review of all 44 research reports published between 2000 and 2018 on bereavement program evaluation was undertaken to identify evaluation methods and assess their apparent efficacy. Bereavement program evaluations varied considerably, with multiple data collection methods per study common (61.4%) over single methods (38.6%). Among these evaluation methods, a self-devised questionnaire was most often used (59.1%), followed by qualitative interviewing (36.4%), and the use of 1 or more of 35 data collection instruments such as grief inventories or depression scales (40.9%). Evaluative data were usually only collected once (77.3%), typically around program completion. Formal bereavement program evaluation appears to be ad hoc and sporadic, and potentially unlikely to provide the type and quality of information needed to retain, improve, expand, or abandon programs. Evaluation method developments including evaluation standards are needed to ensure recipients and others benefit as expected from bereavement programs.
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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 | 0.018 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.037 | 0.031 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".