Bibliographic record
Abstract
Peter Drucker pointed out an important distinction between 'doing things right' and 'doing the right thing', which recognised that all problems are embedded in a context and thus can only be understood within their unique contextual setting. Contemporary research practices in clinical medicine often regards contextual factors as potential confounders that will bias effect estimates and thus must be avoided. However rigorous, research devoid of context ultimately deprives users of understanding of the support factors that make research transferable to policy decisions or managing care of individual patients-it stands in the way of 'doing the right thing' in 'real life' settings. Appreciating that all problems are embedded in a greater context means that one should not ignore their interconnected and interdependent systemic nature, that is, every variable is simultaneously dependent and independent. This is the reason for the cascading effects and feedback loops witnessed in disease progression and policy efforts. We discuss the need for researchers to a-priori consider the context of their research question as well as the structural relationships of the variables under investigation, which in turn provides the basis for choosing the most appropriate research design. We have a moral imperative to first 'do the right thing'-ask questions that address the contextual needs of our patients, and then to 'do it right'-choose the best research method to answer this contextually framed need. Only then will our research efforts have meaningful and lasting impacts on patient care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.148 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.095 |
| Scholarly communication | 0.019 | 0.031 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.012 | 0.030 |
| Insufficient payload (model declined to judge) | 0.004 | 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".