Codesigned standardised referral form: simplifying the complexity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Referring providers are often critiqued for writing poor-quality referrals. This study characterised clinical referral guidelines and forms to understand which data consultant providers require. These data were then used to codesign an evidence-based, high-quality referral form. METHODS: This study used both observational and quality improvement approaches. Canadian referral guidelines were reviewed and summarised. Referral data fields from 150 randomly selected Ontario referral forms were categorised and counted. The referral guideline summary and referral data were then used by referring providers, consultant providers and administrators to codesign a referral form. RESULTS: Referral guidelines recommended 42 types of referral data be included in referrals. Referral data were categorised as patient demographics, provider demographics, reason for referral, clinical information and administrative information. The percentage of referral guidelines recommending inclusion of each type of referral data varied from 8% to 77%. Ontario referral forms requested 264 different types of referral data. Digital referral forms requested more referral data types than paper-based referral forms (55.0±10.6 vs 30.5±8.1; 95% CI p<0.01). A codesigned referral form was created across two sessions with 29 and 21 participants in each. DISCUSSION: Referral guidelines lack consistency and specificity, which makes writing high-quality referrals challenging. Digital referral forms tend to request more referral data than paper-based referrals, which creates administrative burdens for referring and consultant providers. We created the first codesigned referral form with referring providers, consultant providers and administrators. We recommend clinical adoption of this form to improve referral quality and minimise administrative burdens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it