Systems thinking: a different window on the world of implementation and global exchange of behavioral medicine evidence
Bibliographic record
Abstract
Practice: Practitioners embedded within a local context hold key knowledge elements and are critical participants for research translation efforts. Policy: Policies encouraging flexibility and local context will be critical for the success of research translation efforts. A key issue will be allocation of adequate resources to support the translation and implementation process. Research: Suggestions are made for changes in the way researchers think about, and approach, the conduct and translation of research, including the embrace of systems thinking. Building solid and user-friendly bridges from the science of the developed world to the realities of low- to middle-income counties is a “wicked problem” [14]. Not only are there profound differences in culture and context but we also may need fundamental change in how we think about the very nature of our science and how we work to improve implementation and exchange. An essential first step is to identify the major issues and begin the process of creating a common language and logic [1–3]. This brief commentary is intended as a beginning point for that journey.
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 | 0.090 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.012 | 0.095 |
| Scholarly communication | 0.043 | 0.060 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.019 | 0.050 |
| Insufficient payload (model declined to judge) | 0.009 | 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".