Bibliographic record
Abstract
OBJECTIVE: To create an electronic procedural logbook with enhanced user interactivity and usefulness as an educational resource. DESCRIPTION: From our own work on electronic student logbooks and other studies, it is clear that compliance with complete and valid data entry remains a challenge. Without direct and visible benefit, students are reluctant to spend time logging all their cases. We developed an interactive procedural logbook using Microsoft Embedded Visual Basic and Metrowerks CodeWarrior. Interface design focused on rapid data entry with minimum requirement for text, and field-level automation where possible. We loaded it onto a mixed platform of personal digital assistants (PDAs)--Compaq iPaq Pocket PCs and HandEra 330 PalmOS devices. Our rural residents were supplied with the devices of their choice. Various built-in educational reference resources included: (1) contextual help options about each procedure, which contained diagrams and pictures; (2) diagnostic and fee coding so they could see how poorly some procedures pay; and (3) Quick Tips relevant to each procedure, which can be easily modified by the preceptor. Preceptor evaluations and comments can be entered rapidly. Using built-in database conduits, data are automatically collected from each device on every HotSync with the desktop. Data can then be collated and analyzed using Microsoft Access or via secure Web access. DISCUSSION: Improved compliance has been dramatic-one resident logged 250 procedures in just two months. However, not all residents have been successful in establishing seamless synchronization, and the resulting data loss has caused frustration. The evidence indicates the need to implement central data collection and backup right from the outset. Central data collection provides many advantages. The program director has better information for future applications. Preceptor evaluations are now spread over many interactions and yet can be collated and analyzed. Quick Tips have been very popular-we have been able to collect the tips and redistribute them. Focus-group feedback from the residents has shown that the rich data in the logbook's reference component improved its usefulness and popularity as an educational tool. Choice of device type is important for user acceptance because devotees of one platform are reluctant to switch to another. Cross-platform development does slow the process but is increasingly easy with the latest software design tools, such as AppForge. These new tools have enabled us to explore further improvements in data entry. Digital ink provides the ability to capture annotated diagrams and preceptor signatures. Voice input is built in with these devices, and our software now allows for voice annotation for more detailed commentary by preceptors or student. The compressed digital sound file is collected along with the data and transcribed centrally (on-device voice recognition is not feasible yet). Point-of-care accessibility has been the key attraction of using these devices for logging encounter data. This project demonstrates that it is important to explore all multimodal interactive capabilities to provide a truly rich educational tool.
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 | 0.012 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.109 | 0.047 |
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".