Improving access to emergent spinal care through knowledge translation: an ethnographic study
Bibliographic record
Abstract
BACKGROUND: For patients and family members, access to timely specialty medical care for emergent spinal conditions is a significant stressor to an already serious condition. Timing to surgical care for emergent spinal conditions such as spinal trauma is an important predictor of outcome. However, few studies have explored ethnographically the views of surgeons and other key stakeholders on issues related to patient access and care for emergent spine conditions. The primary study objective was to determine the challenges to the provision of timely care as well as to identify areas of opportunities to enhance care delivery. METHODS: An ethnographic study of key administrative and clinical care providers involved in the triage and care of patients referred through CritiCall Ontario was undertaken utilizing standard methods of qualitative inquiry. This comprised 21 interviews with people involved in varying capacities with the provision of emergent spinal care, as well as qualitative observations on an orthopaedic/neurosurgical ward, in operating theatres, and at CritiCall Ontario's call centre. RESULTS: Several themes were identified and organized into categories that range from inter-professional collaboration through to issues of hospital-level resources and the role of relationships between hospitals and external organizations at the provincial level. Underlying many of these issues is the nature of the medically complex emergent spine patient and the scientific evidentiary base upon which best practice care is delivered. Through the implementation of knowledge translation strategies facilitated from this research, a reduction of patient transfers out of province was observed in the one-year period following program implementation. CONCLUSIONS: Our findings suggest that competing priorities at both the hospital and provincial level create challenges in the delivery of spinal care. Key stakeholders recognized spinal care as aligning with multiple priorities such as emergent/critical care, medical through surgical, acute through rehabilitative, disease-based (i.e. trauma, cancer), and wait times initiatives. However, despite newly implemented strategies, there continues to be increasing trends over time in the number of spinal CritiCall Ontario referrals. This reinforces the need for ongoing inter-professional efforts in care delivery that take into account the institutional contexts that may constrain individual or team efforts.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| 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".