An exploratory analysis of the nature of informal knowledge underlying theories of planned action used for public health oriented knowledge translation
Bibliographic record
Abstract
BACKGROUND: Informal knowledge is used in public health practice to make sense of research findings. Although knowledge translation theories highlight the importance of informal knowledge, it is not clear to what extent the same literature provides guidance in terms of how to use it in practice. The objective of this study was to address this gap by exploring what planned action theories suggest in terms of using three types of informal knowledge: local, experiential and expert. We carried out an exploratory secondary analysis of the planned action theories that informed the development of a popular knowledge translation theory. Our sample included twenty-nine (n = 29) papers. We extracted information from these papers about sources of and guidance for using informal knowledge, and then carried out a thematic analysis. RESULTS: We found that theories of planned action provide guidance (including sources of, methods for identifying, and suggestions for use) for using local, experiential and expert knowledge. CONCLUSION: This study builds on previous knowledge translation related work to provide insight into the practical use of informal knowledge. Public health practitioners can refer to the guidance summarized in this paper to inform their decision-making. Further research about how to use informal knowledge in public health practice is needed given the value being accorded to using informal knowledge in public health decision-making processes.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.093 | 0.250 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".