Measurement and Analysis of Individualized Care Inventory Responses Comparing Long-Term Care Nurses and Care Aides
Bibliographic record
Abstract
PURPOSE: Motivating and enabling formal caregivers to provide individualized resident care has become an increasingly important objective in long-term care (LTC) facilities. The current study set out to examine the structure of responses to the individualized care inventory (ICI). DESIGN AND METHODS: Samples of 242 registered nurses (RNs)/licensed practical nurses (LPNs) and 326 care aides were recruited from 54 LTC facilities in 3 of 5 British Columbia health authorities. Baseline confirmatory factor analytic (CFA) models were computed separately for RNs/LPNs and care aides; invariance analyses were next undertaken to compare these CFA models. RESULTS: For both RNs/LPNs and care aides, support was found for a 4-factor model of ICI responses mapping onto a higher order individualized care (IC) construct. This model was largely equivalent between formal caregiver groups, although the relative contribution of certain first-order factors differed between the two. Of further note, both groups appear to interpret and respond to 31 of 35 ICI items in a similar manner. IMPLICATIONS: The results of this study provide further support for the psychometric properties of ICI responses. Although further research is required, the ICI appears to be an appropriate self-report measure. This instrument may be used by researchers, policymakers, administrators, and practitioners alike to assess strengths as well as areas for improving the delivery of IC to LTC residents by formal caregivers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".