Number Needed to Benefit From Information (NNBI): Proposal From a Mixed Methods Research Study With Practicing Family Physicians
Bibliographic record
Abstract
PURPOSE: We wanted to describe family physicians' use of information from an electronic knowledge resource for answering clinical questions, and their perception of subsequent patient health outcomes; and to estimate the number needed to benefit from information (NNBI), defined as the number of patients for whom clinical information was retrieved for 1 to benefit. METHODS: We undertook a mixed methods research study, combining quantitative longitudinal and qualitative research studies. Participants were 41 family physicians from primary care clinics across Canada. Physicians were given access to 1 electronic knowledge resource on handheld computer in 2008-2009. For the outcome assessment, participants rated their searches using a validated method. Rated searches were examined during interviews guided by log reports that included ratings. Cases were defined as clearly described searches where clinical information was used for a specific patient. For each case, interviewees described information-related patient health outcomes. For the mixed methods data analysis, quantitative and qualitative data were merged into clinical vignettes (each vignette describing a case). We then estimated the NNBI. RESULTS: In 715 of 1,193 searches for information conducted during an average of 86 days, the search objective was directly linked to a patient. Of those searches, 188 were considered to be cases. In 53 cases, participants associated the use of information with at least 1 patient health benefit. This finding suggested an NNBI of 14 (715/53). CONCLUSION: The NNBI may be used in further experimental research to compare electronic knowledge resources. A low NNBI can encourage clinicians to search for information more frequently. If all searches had benefits, the NNBI would be 1. In addition to patient benefits, learning and knowledge reinforcement outcomes are frequently reported.
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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 | 0.192 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".