Clarifying a working definition for ‘precision communication’: a scoping review of medical literature on communication
Bibliographic record
Abstract
AIMS: While "tailored communication" and "precision medicine" have been well-defined in medical literature, the concept of "precision communication" in healthcare has yet to be clarified. We sought to review how "precision communication" has been used in the medical literature to date and propose a working definition for this term. MATERIALS & METHODS: We searched seven medical literature databases from inception until 22 May 2024, for articles using terms related to "precision communication." Multiple reviewers screened titles/abstracts and full-texts; an initial pool of full-text articles underwent thematic analysis to clarify relevant themes for inclusion. Data regarding the use of the term "precision communication" were manually charted and analyzed descriptively. RESULTS: Of the 7,648 articles identified, 21 full-text articles were included in the final descriptive analysis. These articles highlighted the personalization of tailored communication to patient characteristics, its impact on clinical outcomes, and the recipients of "precision communication." The latter may distinguish "precision communication" from similar terms: where "tailored communication" was mostly applied to undefined groups, we propose that "precision communication" is precise toward specific patient subpopulations, paralleling the use of genomics in precision medicine. CONCLUSIONS: From this review, we defined precision communication as "the personalization of communication to subpopulations."
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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 | 0.119 | 0.258 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.043 | 0.029 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".