Four levels of outcomes of information‐seeking: A mixed methods study in primary health care
Bibliographic record
Abstract
Primary health care practitioners routinely search for information within electronic knowledge resources. We proposed four levels of outcomes of information‐seeking: situational relevance, cognitive impact, information use, and patient health outcomes. Our objective was to produce clinical vignettes for describing and testing these levels. We conducted a mixed methods study combining a quantitative longitudinal study and a qualitative multiple case study. Participants were 10 nurses, 10 medical residents, and 10 pharmacists. They had access to an online resource, and did 793 searches for treatment recommendations. Using the Information Assessment Method ( IAM ), participants rated their searches for each of the four levels. Rated searches were examined in interviews guided by log reports and a think‐aloud protocol. Cases were defined as clearly described searches where clinical information was used for a specific patient. For each case, interviewees described the four levels of outcomes. Quantitative and qualitative data were merged into clinical vignettes. We produced 130 clinical vignettes. Specifically, 46 vignettes (35.4%) corresponded to clinical situations where information use was associated with one or more than one type of positive patient health outcome: increased patient knowledge ( n = 28), avoidance of unnecessary or inappropriate intervention ( n = 25), prevention of disease or health deterioration ( n = 9), health improvement ( n = 6), and increased patient satisfaction ( n = 3). Results suggested information use was associated with perceived benefits for patients. This may encourage clinicians to search for information more often when they feel the need. Results supported the four proposed levels of outcomes, which can be transferable to other information‐seeking contexts.
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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 | 0.050 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".