Bibliographic record
Abstract
BACKGROUND: Integrated knowledge translation (IKT) flows from the premise that knowledge co-produced with decision-makers is more likely to inform subsequent decisions. However, evaluations of manager/policy-maker-focused IKT often concentrate on intermediate outcomes, stopping short of assessing whether research findings have contributed to identifiable organisational action. Such hesitancy may reflect the difficulty of tracing the causes of this distal, multifactorial outcome. This paper elucidates how an approach based on realistic evaluation could advance the field. MAIN TEXT: Realistic evaluation views outcomes as a joint product of intervention mechanisms and context. Through identification of context-mechanism-outcome configurations, it enables the systematic testing and refinement of 'mid-range theory' applicable to diverse interventions that share a similar underlying logic of action. The 'context-sensitive causal chain' diagram, a tool adapted from the broader theory-based evaluation literature, offers a useful means of visualising the posited chain from activities to outcomes via mechanisms, and the context factors that facilitate or disrupt each linkage (e.g. activity-mechanism, mechanism-outcome). Drawing on relevant literature, this paper proposes a context-sensitive causal chain by which IKT may generate instrumental use of research findings (i.e. direct use to make a concrete decision) and identifies an existing tool to assess this outcome, then adapts the chain to describe a more subtle, indirect pathway of influence. Key mechanisms include capacity- and relationship-building among researchers and decision-makers, changes in the (perceived) credibility and usability of findings, changes in decision-makers' beliefs and attitudes, and incorporation of new knowledge in an actual decision. Project-specific context factors may impinge upon each linkage; equally important is the organisation's absorptive capacity, namely its overall ability to acquire, assimilate and apply knowledge. Given a sufficiently poor decision-making environment, even well-implemented IKT that triggers important mechanisms may fall short of its desired outcomes. Further research may identify additional mechanisms and context factors. CONCLUSION: By investigating 'what it is about an intervention that works, for whom, under what conditions', realistic evaluation addresses questions of causality head-on without sacrificing complexity. A realist approach could contribute greatly to our ability to assess - and, ultimately, to increase - the value of IKT.
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 | 0.410 | 0.571 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.025 | 0.030 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".