Towards a person-centred holistic consultation framework for paramedics attending non-acute presentations: A multidisciplinary commentary
Bibliographic record
Abstract
Introduction: Traditional rapid-assessment models may not be fully suited for the complex non-acute presentations that paramedics commonly encounter. As paramedicine evolves to meet non-acute patient needs, extended practice community paramedic roles are emerging to deliver holistic and collaborative community-based healthcare. Purpose: This commentary outlines the need for a person-centred holistic clinical consultation framework for paramedics attending complex non-acute presentations. A framework that provides a structured and systematic process for paramedics, particularly those who are transitioning into extended practice community paramedic roles, to guide them in conducting holistic person-centred assessments. This commentary highlights the importance of understanding the biopsychosocial factors influencing patient wellbeing and provides a conceptual foundational framework as an example for the paramedicine community to further develop and validate. Relevance: A standardised systematic consultation approach supports more effective communication, shared decision-making, and safe care planning. Such a framework aligns with professional standards to enhance interprofessional collaboration and improve health literacy and equity for structurally marginalised patients. Outcome: We propose a nine-phase clinical consultation framework for non-acute presentations to improve accuracy in clinical reasoning, paramedic confidence, and patient outcomes. Through patient engagement, trust-building, and addressing impacts of the social determinants of health, this framework aims to refine paramedic practice for complex non-acute presentations and support integrated care models. Conclusion: The proposed patient consultation framework aims to guide paramedics toward a structured, comprehensive approach for assessing non-acute presentations bridging the gap between rapid emergency-focused assessment and community-based care for complex and often undifferentiated non-acute presentations. As paramedics develop confidence in exploring each patient's individual biopsychosocial circumstances, they will be better positioned to enhance patient experiences and outcomes, further contributing meaningfully to their continuum of care.
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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 | 0.058 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.032 | 0.047 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".