Meaningful Messages From Grief Workshop Participants
Bibliographic record
Abstract
Although there is an increased need for delivery of bereavement care, many health care providers in acute care hospital settings feel inadequately prepared to deliver quality grief support, have lack of time, and have inexperience in provision of bereavement care. As a result, although families would like health care providers to offer bereavement support, they are inadequately trained and susceptible to burnout, resulting in families not having their needs met. The purpose of this qualitative study was to uncover the social process occurring in a bereavement education workshop titled "How to Care, What to Say" offered to health care providers. The goal of the workshop was to improve delivery of care for the dying and their family by providing holistic care to the family before, during, and after the death of a loved one. Past grief workshop participants who cared for the bereaved were interviewed, and data were analyzed and synthesized using constructivist grounded theory. Individual interviews and focus group data revealed participants' perceptions, learnings, and potential integration of the workshop into practice. The overarching theory of providing bereavement care that emerged from the data is "a relational process of understanding knowledge, self-awareness, moral responsibilities, and advancing grief competencies of providing holistic grief support."
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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".