Defaulters in general practice: who are they and what can be done about them?
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: The study of patients in primary care settings who default on their appointment has been based largely on short-term surveys in individual health centres. OBJECTIVE: As part of a wider research project into the potential of practice computer appointment systems as a data source, we wanted to explore the aggregate pattern of default. METHOD: Comprehensive computer appointment data from nine general practices for 1 or 2 years were analysed to explore the pattern of defaulted appointments for doctors and practice nurses. RESULTS: Around 6.5% of all appointments ended in a default. Default rates were found to be highest amongst young adults and, at a practice level, to be highly correlated with deprivation level. About two-thirds of those who defaulted only did it once during the year. A small core of patients defaulted frequently, but only a quarter of these repeated their behaviour in the following year. CONCLUSIONS: The discussion suggests that strategies based on educating or punishing defaulters in order to change their behaviour may be of limited effectiveness.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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