general practice research network
Bibliographic record
Abstract
Background: Primary care research networks have been established internationally since the 1960s to enable diverse practitioners to engage in and develop research and education and implement research evidence. The newly established Western Research and Education Network (WestREN) is one such network consisting of a collaboration between the Discipline of General Practice at NUI Galway and 71 West of Ireland general practices. In September 2009 all member practices were issued with a questionnaire with two objectives: to describe the structure and characteristics of the member practices and to compare the results to the national profile of Irish general practice. Methods: A postal survey was used followed by one written and one email reminder. Results: A response rate of 73 % (52/71) was achieved after two reminders. Half of practices were in a rural location, one quarter located in an urban setting and another quarter in a mixed location. Ninety-four per cent of general practitioners practice from purpose-built or adapted premises with under 6 % of practices being attached to the general practitioner’s residence. Over 96 % of general practitioners use appointment systems with 58 % using appointment only.
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.
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 | 0.023 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.418 | 0.235 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".