Consulting With First Nations Communities to Develop Text‐Based Support for Grieving Fathers
Bibliographic record
Abstract
BACKGROUND: The loss occasioned through miscarriage, stillbirth or neonatal death is recognised as a traumatic event causing grief and sorrow in fathers. While the rate of Indigenous perinatal deaths is almost twice that of non-Indigenous, there is little support available for Aboriginal and Torres Strait Islander grieving fathers. OBJECTIVE: The SMS4DeadlyDads team partnered with Red Nose, the national charity supporting grieving parents, to co-design text-based support for grieving fathers with community representatives and clinicians. DESIGN: A 2-year consultation process with Indigenous services and stakeholders took place in urban and remote locations in Australia. The support for fathers following perinatal loss was assessed, and messages were adapted from those for non-Indigenous fathers and evaluated. Final messages were reviewed by Red Nose clinicians for optimal delivery timing. RESULTS: Community representatives noted the lack of support for new fathers. The culturally appropriate SMS4Deadlydads service delivering text messages to new fathers' mobile phones was welcomed as 'something for dads' and the potential to provide confidential support in cases of perinatal loss was recognised. The resulting set of messages was acceptable to indigenous and non-Indigenous stakeholders. CONCLUSIONS: The successful development of the messages for Indigenous fathers demonstrates that respectful consultation led by experienced Indigenous leaders can ensure cultural safety and gain community commitment to address highly sensitive topics. PUBLIC CONTRIBUTION: Indigenous community representatives and stakeholder service were involved in deciding on the value of the text messaging approach to fathers' grief, the identification of message topics, the wording used in the texts and the linked resources.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.002 |
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".