Insights into the Implementation and Operation of a Novel Paramedic Long-term Care Program
Bibliographic record
Abstract
OBJECTIVE: An extended-care paramedic (ECP) program was implemented to provide emergency assessment and care on site to long-term care (LTC) residents suffering acute illness or injury. A single paramedic works collaboratively with physicians, LTC staff, patient, and family to develop care plans to address acute situations, often avoiding the need to transport the resident to hospital. We sought to identify insights gained and lessons learned during implementation and operation of this novel program. METHODS: The perceptions and experiences of various stakeholders were explored in focus groups, using a semi-structured interview guide. Two investigators independently conducted thematic analysis and identified emerging themes and related codes. Congruence and differences were discussed to achieve consensus. RESULTS: Twenty-one participants took part in four homogeneous focus groups: paramedics and dispatchers, ECPs, ECP oversight physicians, and decision-makers. The key themes identified were (1) program implementation, (2) ECP process of care, (3) communications, and (4) end-of-life care. CONCLUSION: The ECP program has positive implications for the relationship between EMS and LTC, requires additional paramedic training, and can positively affect LTC patient experiences during acute medical events. ECPs have a novel role to play in end-of-life care and find this new role rewarding.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".