Development and evaluation of a low-cost database solution for the Community Paramedicine at Clinic (CP@clinic) database
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
The Community Paramedicine at Clinic (CP@clinic) program is a community program that utilizes community paramedics to support older adults in assessing their risk factors, managing their chronic conditions, and linking them to community resources. The aim of this project is to design a low-cost, portable, secure, user-friendly database for CP@clinic sessions and pilot test the database with paramedics and older adult volunteers. The CP@clinic program database using the Microsoft Access software was first developed through consultation with the CP@clinic research team. Next, the database was pilot tested with two sets of older adults and one set of paramedics to assess user experience. Volunteers completed a survey regarding their perceptions of the level of difficulty when using the database. A computer-based database was the best option as it provided flexibility while reducing costs. The final database should perform calculations and summarize risk assessment data, provide recommended resources, generate automated reports, capture changes in medical and medication history, and ensure that the sensitive information is secure. During pilot testing, the older adult participants and the paramedics indicated that the database was easy to use. This low-cost, user-friendly and secure database captures initial and follow-up data, incorporates algorithms that guide the paramedics, and calculates risk factor scores for the participants. This solution to a healthcare database is translatable to other health research studies in which ongoing patient data is collected electronically and longitudinally.
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 | 0.010 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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