Towards better measures of research utilization: a collaborative study in Canada and Sweden
Bibliographic record
Abstract
AIMS: This paper is a report of a study examining research utilization in nursing. The specific aims were to (1) clarify the construct of research utilization, and (2) identify observable indicators of research utilization. BACKGROUND: Robust measures of research utilization do not exist despite steadily increasing numbers of studies in the field. One reason for this is theoretical confusion surrounding the central concepts in the field. METHOD: A qualitative (focus group) design was used to explore the construct of research utilization in two countries: Canada and Sweden. A systematic and sequential (three phases) approach to expert sampling framed the study. Phase 1 consisted of initial construct clarification by the research team (2005). In Phase 2, a face-to-face meeting with a panel of international research utilization nursing experts was held (2005). Phase 3 consisted of a series of focus groups with nursing care (direct and non-direct) providers (2005-2007). Data were analysed using content analysis. FINDINGS: The nursing care providers did not commonly use the term 'research utilization'. Several examples of research utilization were provided; a majority of these examples related to instrumental research utilization and became increasingly concrete as one moved from non-direct to direct care participants. Participants identified several indicators of research utilization (instrumental and conceptual). From these indicators, a measurement schematic was derived. CONCLUSIONS: The construct of research utilization is multi-faceted. Several indicators of research utilization were identified, which can be used to augment existing or develop a new and improved measure that taps both instrumental and conceptual use.
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 | 0.079 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.025 | 0.006 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".