Consensus Development Conference: Promoting Access to Quality Palliative Care in Canada
Bibliographic record
Abstract
OBJECTIVE: This article outlines the development and implementation of a consensus development conference (CDC). BACKGROUND: As a rapid method for data synthesis, a CDC affords a timely and methodic means of evaluating data to effect change in healthcare policy. METHODS: The CDC methodology was adopted for the Palliative Care Matters initiative due to its engagement with the public, scientific community, and palliative care stakeholders. RESULTS: It requires the involvement of seven key groups/roles to successfully effect change: a manager, steering committee, scientific expert panel, public lay panel, a lay-panel facilitator, a public audience, and the media for dissemination. DISCUSSION: This article also details the background information and guiding principles on which the Palliative Care Matters initiative was formed. A Canadian Reference Working Group was formed to develop the Palliative Care Matters guiding principles into six scientific questions. The scientific articles in this supplemental issue each present evidence and expert recommendations that speak to one of the Palliative Care Matters scientific questions.
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 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 | 0.199 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.005 | 0.007 |
| 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".