Feedback reporting of survey data to healthcare aides
Bibliographic record
Abstract
BACKGROUND: This project occurred during the course of the Translating Research in Elder Care (TREC) program of research. TREC is a multilevel and longitudinal research program being conducted in the three Canadian Prairie Provinces of Alberta, Saskatchewan, and Manitoba. The main purpose of TREC is to increase understanding about the role of organizational context in influencing knowledge use in residential long-term care settings. The purpose of this study was to evaluate healthcare aides' (HCAs) perceptions of a one-page poster designed to feed back aggregated data (including demographic information and perceptions about influences on best practice) from the TREC survey they had recently completed. METHODS: A convenience sample of 7 of the 15 nursing homes participating in the TREC research program in Alberta were invited to participate. Specific facility-level summary data were provided to each facility in the form of a one-page poster report. Two weeks following delivery of the report, a convenience sample of HCAs was surveyed using one-to-one structured interviews. RESULTS: One hundred twenty-three HCAs responded to the evaluation survey. Overall, HCAs' opinions about presentation of the feedback report and the understandability, usability, and usefulness of the content were positive. For each report, analysis of data and production and inspection of the report took up to one hour. Information sessions to introduce and explain the reports averaged 18 minutes. Two feedback reports (minimum) were supplied to each facility at a cost of CAN$2.39 per report, for printing and laminating. CONCLUSIONS: This study highlights not only the feasibility of producing understandable, usable, and useful feedback reports of survey data but also the value and importance of providing feedback to survey respondents. More broadly, the findings suggest that modest strategies may have a positive and desirable effect in participating sites.
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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 | 0.073 | 0.230 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".