Which journals do primary care physicians and specialists access from an online service?
Bibliographic record
Abstract
OBJECTIVE: The study sought to determine which online journals primary care physicians and specialists not affiliated with an academic medical center access and how the accesses correlate with measures of journal quality and importance. METHODS: Observational study of full-text accesses made during an eighteen-month digital library trial was performed. Access counts were correlated with six methods composed of nine measures for assessing journal importance: ISI impact factors; number of high-quality articles identified during hand-searches of key clinical journals; production data for ACP Journal Club, InfoPOEMs, and Evidence-Based Medicine; and mean clinician-provided clinical relevance and newsworthiness scores for individual journal titles. RESULTS: Full-text journals were accessed 2,322 times by 87 of 105 physicians. Participants accessed 136 of 348 available journal titles. Physicians often selected journals with relatively higher numbers of articles abstracted in ACP Journal Club. Accesses also showed significant correlations with 6 other measures of quality. Specialists' access patterns correlated with 3 measures, with weaker correlations than for primary care physicians. CONCLUSIONS: Primary care physicians, more so than specialists, chose full-text articles from clinical journals deemed important by several measures of value. Most journals accessed by both groups were of high quality as measured by this study's methods for assessing journal importance.
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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 | 0.003 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".