Essential Elements to Implementing a Paramedic Palliative Model of Care: An Application of the Consolidated Framework for Implementation Research
Bibliographic record
Abstract
Background: Comfort care without transport to hospital was not traditionally a paramedic practice. The novel Paramedics Providing Palliative Care at Home Program includes a new clinical practice guideline, medications, a database to manage and share goals of care, and palliative care training. This study determined essential elements for implementation, scale, and spread of this Program. Methods: Deliberative dialogs, a qualitative method, were held with diverse stakeholders/experts in one province with the Program (Nova Scotia, March 2018) and one without (British Columbia, July 2018). The Consolidated Framework for Implementation Research (CFIR) informed the discussion guide and was used in a framework analysis. Four team members analyzed the data independently; themes were derived by consensus with the broader research team. Results: CFIR constructs framed several key elements. Inter-sectoral communication is critical but challenged by privacy concerns and the siloed structure of the health system. Locally adapted training is an essential characteristic of the intervention; cost is a factor. A shift in mindset away from traditional paramedic roles is required; this can be facilitated by paramedic champions and a positive implementation climate. Early engagement of diverse stakeholders and planning for sustainability is key. Conclusion: This framework analysis using CFIR constructs can guide successful scale and spread of the program. The constructs of Outer setting: Cosmopolitanism; Characteristics of the intervention: Adaptability; Inner Setting: Implementation climate; and Processes: Engagement, and Planning, emerged as essential.
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 | 0.403 | 0.298 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.011 | 0.025 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".