Grief and grief support needs in Canada: A mixed methods protocol
Bibliographic record
Abstract
Background: In their lifetime, every person will experience the loss of someone they care about. In Canada, the COVID-19 pandemic, the ongoing opioid crisis, and the discovery of unmarked graves at residential schools have brought this into particular focus. Research and theory in the area of grief have evolved over the years. Grief literacy challenges us to better understand and support grief in all aspects of our society. The Public Health Model of Bereavement Support was theorized and tested in Australia. The supports people seek are explored and the model identifies low, medium, and high categories of risk of prolonged grief disorder. Objective: The purpose of this study is to advance public health understanding of grief and its support. The specific research objectives are to (1) test the Public Health Model of Bereavement Support in the Canadian context and (2) build a grounded theory of grief support. Design: This project uses a sequential mixed methods design. Methods: A Canada-wide survey in English and French will produce data that will be used to empirically test the Public Health Model of Bereavement Support. In the second phase, the grounded theory of grief support centers on voices that have not been widely heard in grief research. The mixed methods then fully elucidate grief and grief support in Canada. Results: This is the first study internationally to test this model in a (post)pandemic context, in a jurisdiction that legally permits medical assistance in dying, and in a context with an opioid crisis. Conclusion: The findings will allow us to better understand grief and the current realities of grieving, which has the potential to enhance the wellbeing of the millions of Canadians who are grieving.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".