Spread, participant experience, and implementation of Pallium Canada’s Palliative Care ECHO project: a mixed methods study
Bibliographic record
Abstract
BACKGROUND: Pallium Canada's Palliative Care Extension for Community (ECHO) Project (PC ECHO) is a five-year national initiative (launched April 2021) to support continuous professional development and to facilitate the integration of palliative care across different care settings. PC ECHO includes a superhub (Pallium Canada) and several partner hubs. The goal of this formative evaluation study is to explore the project's early spread and the experiences of hub partners and participants from April 2021 to September 2023. METHODS: A mixed-methods approach was used. Webinar and participant demographic information was collected by Pallium Canada's online learning management system and by partner hubs. Participants' experience feedback was collected through a standardized online evaluation form. Project leads at the superhub and three inaugural partners were interviewed and transcriptions subjected to a thematic analysis. RESULTS: A total of 301 sessions were delivered during the study period; 155 (51%) by Pallium Canada and 146 (49%) by nine partner hubs. Of these, 125 (42%) were standalone-type sessions and 176 (58%) were community of practice (COP)- or series-type sessions. A total of 7648 individuals - representing over 17 professions - participated across the 301 sessions; the nursing professions were the largest group (36.8%). There was a total of 17,467 participations across the 301 sessions, with participants from across Canada and 31% from rural or small population centres. 5105 evaluations of sessions were received (response rate 29%). Of these, 90% stated they "Agreed" or "Strongly Agreed" that the sessions were good learning experiences, and 93% indicated that they would recommend the session to colleagues. Project strengths and facilitators included accelerating partners' palliative care mandates, increased connections to other partners and resources, flexibility with the ECHO model, and funding received. CONCLUSION: The intended outcomes of the PC ECHO Project are materializing, including utility for participants and helping partner hubs accelerate their palliative care mandates. There is evidence of significant spread, over a relatively short period of time. Future studies should include further exploration of the respective roles and impact of different session types (standalone versus communities of practice and series) and impact at higher patient and health system levels.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".