Barriers and enablers to implementing a virtual tertiary-regional Telemedicine Rounding and Consultation (TRAC) model of inpatient pediatric care using the Theoretical Domains Framework (TDF) approach: a study protocol
Bibliographic record
Abstract
BACKGROUND: Over-occupancy at the two tertiary pediatric care hospitals in Alberta, Canada is steadily increasing with simultaneous decline in occupancy of pediatric beds at regional hospitals. Over-occupancy negatively impacts timeliness and potentially, the safety of patient care provided at these two tertiary hospitals. In contrast, underutilization of pediatric beds at regional hospitals poses the risk of losing beds provincially, dilution of regional pediatric expertise and potential erosion of confidence by regional providers. One approach to the current situation in provincial pediatric care capacity is development of telemedicine based innovative models of care that increase the population of patients cared for in regional pediatric beds. A Telemedicine Rounding and Consultation (TRAC) model involves discussing patient care or aspects of their care using telemedicine by employing visual displays, audio and information sharing between tertiary and regional hospitals. To facilitate implementation of a TRAC model, it is essential to understand the perceived barriers among its potential users in local context. The current study utilizes qualitative methodologies to assess these perceived clinician barriers to inform a future pilot and evaluation of this innovative virtual pediatric tertiary-regional collaborative care model in Alberta. METHODS: We will use a qualitative descriptive design guided by the Theoretical Domain Framework (TDF) to systematically identify the tertiary and regional clinical stakeholder's perceived barriers and enablers to the implementation of proposed TRAC model of inpatient pediatric care. Semi-structured interviews and focus groups with pediatricians, nurses and allied health professionals, administrators, and family members will be conducted to identify key barriers and enablers to implementation of the TRAC model using TDF. Appropriate behaviour change techniques will be identified to develop potential intervention strategies to overcome identified barriers. These intervention strategies will facilitate implementation of the TRAC model during the pilot phase. DISCUSSION: The proposed TRAC model has the potential to address the imbalance between utilization of regional and tertiary inpatient pediatric facilities in Alberta. Knowledge generated regarding barriers and enablers to the TRAC model and the process outlined in this study could be used by health services researchers to develop similar telemedicine-based interventions in Canada and other parts of the world.
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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 | 0.029 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".