Six Honest Serving Matters, Teaching Us All We Need to Know About Context in Knowledge Implementation? Comment on "Stakeholder Perspectives of Attributes and Features of Context Relevant to Knowledge Translation in Health Settings: A Multi-country Analysis"
Bibliographic record
Abstract
While context is a vital factor in any attempt to study knowledge translation or implement evidence in healthcare, there is a need to better understand the attributes and relations that constitute context. A recent study by J. Squires et al investigates such attributes and definitions, based on 39 stakeholder interviews across Australia, Canada, the United Kingdom, and the United States. Sixteen attributes, comprising 30 elements suggested as new findings, are proposed as the basis for a framework. This commentary argues for the need to incorporate more perspectives but also suggests an initial taxonomy rather than a framework, comprising a wider range of stakeholders and an enhanced understanding of how context elements are related at different levels and how this affects implementation processes. Aligning with person-centred care, this must include not only professionals but also patients and their next of kin, as partners in shaping more evidence-based healthcare.
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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 | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.050 | 0.050 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".