Nursing home administrators’ perspectives on a study feedback report: a cross sectional survey
Bibliographic record
Abstract
BACKGROUND: This project is part of the Translating Research in Elder Care (TREC) program of research, a multi-level and longitudinal research program being conducted in 36 nursing homes in three Canadian Prairie Provinces. The overall goal of TREC is to improve the quality of care for older persons living in nursing homes and the quality of work life for care providers. The purpose of this paper is to report on development and evaluation of facility annual reports (FARs) from facility administrators' perspectives on the usefulness, meaningfulness, and understandability of selected data from the TREC survey. METHODS: A cross sectional survey design was used in this study. The feedback reports were developed in collaboration with participating facility administrators. FARs presented results in four contextual areas: workplace culture, feedback processes, job satisfaction, and staff burnout. Six weeks after FARs were mailed to each administrator, we conducted structured telephone interviews with administrators to elicit their evaluation of the FARs. Administrators were also asked if they had taken any actions as a result of the FAR. Descriptive and inferential statistics, as well as content analysis for open-ended questions, were used to summarize findings. RESULTS: Thirty-one facility administrators (representing thirty-two facilities) participated in the interviews. Six administrators had taken action and 18 were planning on taking action as a result of FARs. The majority found the four contextual areas addressed in FAR to be useful, meaningful, and understandable. They liked the comparisons made between data from years one and two and between their facility and other TREC study sites in their province. Twenty-two indicated that they would like to receive information on additional areas such as aggressive behaviours of residents and information sharing. Twenty-four administrators indicated that FARs contained enough information, while eight found FARs 'too short'. Administrators who reported that the FAR contained enough information were more likely to take action within their facilities than administrators who reported that they needed more information. CONCLUSIONS: Although the FAR was brief, the presentation of the four contextual areas was relevant to the majority of administrators and prompted them to plan or to take action within their facility.
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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 | 0.018 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".