Factors Influencing the Uptake of Research Evidence in Child Welfare: A Synthesis of Findings from Australia, Canada and Ireland
Bibliographic record
Abstract
This paper draws on three studies conducted in Australia, Canada and Ireland which explore the factors influencing research utilisation in the child protection sector in each country. The paper recognises that research uptake is complicated by a number of factors. It also acknowledges critiques which cite the equally significant influence of ideologies, context, unpredictability, time constraints and political expediency. However, all three studies recognised the increasing importance of evidence‐based practice. The methods used in the three studies were not identical but the frameworks used were sufficiently similar to enable the classification of both common and dissimilar barriers and facilitators to research use. Those which they identified were categorised into four types: individual, organisational, environmental and characteristics relating to the nature of research material. Implications were identified for policy makers, service providers and research producers. The point was made that we now live in a period where unprecedented means of knowledge transfer and exchange provide unique opportunities to improve the lives of children and families. Copyright © 2013 John Wiley & Sons, Ltd. ‘All three studies recognised the increasing importance of evidence‐based practice’ Key Practitioner Messages Avail of opportunities to attend learning events. Draw on research findings when conducting assessments, writing reports, devising intervention plans, evaluating programmes and tendering for funding. Establish links with research centres. Become involved in the conduct of research. Support colleagues (champions) who display particular interest and motivation in the use of research evidence by recognising and/or rewarding effort. Avail of opportunities to undertake further study that includes a research component. ‘Establish links with research centres’
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 | 0.082 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.040 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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, 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".