Many family physicians will not manually update PDA software: anobservational study
Bibliographic record
Abstract
BACKGROUND: In a prospective study to explore connections between clinical information delivery and information retrieval, 41 Canadian family physicians searched an electronic knowledge resource (EKR) as needed for practice. Research software, called the Information Assessment Method (IAM), prompted family physicians to report on the situational relevance, perceived cognitive impact and application of their retrieved information hits. Both the IAM and the EKR needed periodic updating to properly address our research questions. OBJECTIVE: To determine the frequency of software updating when manual or semi-automatic approaches are used by family physicians. METHODS: Each family physician received a handheld computer (PDA) that ran the Windows Mobile 6 operating system. For technical reasons, both the IAM and the EKR were accessed offline on PDA. To update the EKR and the IAM, family physicians were asked to synchronize their PDA to their PC. Updating the IAM was a manual process, whereas updating the EKR was semi-automatic. RESULTS: We found: (1) about 25% of family physicians never or rarely updated PDA software on their own, (2) a large number of software updates were never installed and (3) the semi-automatic method was associated with a small increase in the proportion of installed software updates (58.9% versus 48.6% for the manual method). CONCLUSIONS: When a wireless internet connection is not used to update PDA software, sociotechnical issues complicate mobile data collection and data transfer.
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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".