Health research, teaching and provision of care: applying a new approach based on complex systems and a knowledge translation complexity network model
Bibliographic record
Abstract
© 2016 WIT Press. Despite increased emphasis on the translation of research-based knowledge into practice, studies in the U.S.A. and Australia have found that up to 50 per cent of health care delivered does not accord with evidence-based guidelines. Health research, teaching and practice have traditionally emphasised defined inputs to produce specific, linear outputs and changes in teaching and practice may suffer delays in implementation when required to overcome barriers around spheres of interest. We are exploring a new approach based on the principles of complex systems and networks. In this paper, we used a successful knowledge translation project and a case study of a natural disaster, to model the effective application of these principles to a new health knowledge translation model, the Knowledge Translation Complexity Network. Following the Indian Ocean Tsunami of 2004, there were major challenges in identifying many of the dead. Research identified that Dental Age could be used to estimate the chronological age of unidentified victims up to 20 years of age. However, at the time the existing data were insufficient for this purpose and one author (HL) undertook to lead a knowledge creation and synthesis project. The research was evaluated by peer review, published in a leading journal and was subsequently implemented into practice as an identification tool in both paper and electronic forms. Subsequently the data charts and instructions have been translated into 18 languages and are used internationally in university teaching courses as well as in disaster identifications, with feedback evaluation from users providing further refinement. In conclusion, the development of the dentitions showed the characteristics of a complex adaptive system; of emergence, self-organisation, dynamic interactions, robustness and co-evolution. Further, the Dental Atlas incorporated elements of the key sub-networks of the new Knowledge Translation Complexity Network of problem identification (PI), knowledge creation (KC), knowledge synthesis (KS), implementation (I) and evaluation (E). Investigating real-world examples in this way can both highlight key aspects for future planning and identify gaps for development.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".