Time to shift from systems thinking-talking to systems thinking-action Comment on "Constraints to applying systems thinking concepts in health systems: A regional perspective from surveying stakeholders in Eastern Mediterranean countries"
Bibliographic record
Abstract
A recent International Journal of Health Policy and Management (IJHPM) article by Fadi El-Jardali and colleagues makes an important contribution to the literature on health system strengthening by reporting on a survey of healthcare stakeholders in Low- and Middle-Income Countries (LMICs) about Systems Thinking (ST). The study's main contributions are its confirmation that healthcare stakeholders understand the importance of ST but do not know how to act on that understanding, and the call for collective action by the global community of systems thinkers committed to healthcare improvement. We offer three basic considerations for next steps by this community, derived from our recent work in ST and the related field of Knowledge Translation (KT): resist the temptation to adopt a reductionist approach; recognize not everyone needs to understand ST; and do not wait for everything to be in place before getting started.
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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 | 0.030 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.029 | 0.062 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".