The Digital Library of Health Care Consultations and Simulated Health Care Student Teaching: Protocol for a Repository of Recordings to Support Communication Research
Bibliographic record
Abstract
BACKGROUND: Miscommunication in health care is a major source of poor health outcomes, complaints about health care professionals, and poor patient satisfaction. Recordings from real-life consultations provide valuable data for communication research and education. Additionally, recordings from simulation-based education of health care students can provide valuable data for health care education research. OBJECTIVE: The Digital Library is a data repository supporting high-quality health care communication research. This is the single-source citation for all projects that use the Digital Library in Australia. METHODS: This protocol outlines the logistics and consent process for recording and safely storing the recordings of health care consultations and simulation-based education. The processes are outlined for primary health care settings and health care educational settings as well as for health care narratives from consumers. The repository will be used to answer research questions about health care communication and provide a valuable resource for health care education. RESULTS: Data collection for the Digital Library commenced in 2023 and is ongoing at the time of submission of this protocol. The Digital Library has been approved by Monash University's Human Research Ethics Committee. CONCLUSIONS: The Digital Library will provide a national resource for the study of health care communication in community settings, general practice, and other environments. The health care narratives may be a valuable resource for sharing the patient perspective when living with different conditions. The research that uses this repository will be shared through regular academic channels as well as the community-based dissemination strategies of the National Centre for Healthy Ageing. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/67910.
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 | 0.132 | 0.207 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.144 | 0.064 |
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".