Are Alberta's General Practitioner (GP) to Specialist Referral Pathways Aligned with Existing Principles and Best Practices for Patient Empowerment (PE)?
Bibliographic record
Abstract
Long wait times, inefficient care coordination, and patient disengagement have been identified as significant issues in Canadian specialist care (Liddy et al. 2018). This lack of patient engagement could be attributed to the common belief that patients do not have the right expertise (Baker et al. 2016; Brekke, Nuscheler, and Straume 2007, 18), which seems to be enshrined in the gatekeeping role that Canadian general practitioners (GPs) routinely play when referring patients to specialists (Forrest 2003). Whatever the origins and intents of deploying GPs to control access to specialists, poor performance in the GP to specialist (G2S) pathway not only delays treatment and care (Liddy et al. 2018), but it can also cause unnecessary harms (i.e., pain, stress) to patients (McCarron et al. 2019; Manafo et al. 2018). Indeed, poor transitions between GPs and specialists can lead to negative health outcomes (Yiu et al. 2015, 24), especially when patients face repeated, but necessary, transitions between HCPs throughout their 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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".