EP01.007 Co-developing advance care planning resources: a public engagement approach for Hindi speaking communities in BC
Bibliographic record
Abstract
Background The BC Centre for Palliative Care (BCCPC) evaluated its adapted advance care planning (ACP) resources for Punjabi-speaking members of the South Asian community and found the need for similarly adapted resources in Hindi. To address this need, the ACP Hindi Translation project was initiated in Summer of 2021 to translate ACP resources to Hindi from those previously culturally adapted for South Asian communities. Methods This project used a public engagement approach which included two (2) professional translators and five (5) working group members from across BC who translated and reviewed the materials for cultural appropriateness, reading level, colloquialisms and terms borrowed from other languages. A focus group of ten (10) community members with no background in ACP or healthcare also reviewed the translated materials for cultural appropriateness and overall readability. The resources were then finalized and prepared for web posting. Results The use of a public engagement approach facilitated the inclusion of community voices and perspectives on how ACP can best be understood within the community, introduced a common vocabulary on a difficult topic, and ensured ACP resources can be used in a variety of cultural and linguistic contexts. Among the key takeaways from this project are the strong, ongoing need for access to culturally and linguistically diverse resources, and the importance of public engagement in creating health resources that enable difficult conversations with family, friends and health-care providers in a culturally informed manner. Conclusion The co-development of culturally and linguistically appropriate resources is feasible and creates meaningful results. The public engagement approach used in this project can be applied to other interventions, communities and jurisdictions.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.103 | 0.017 |
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".