Psychometric Development of the Iceland-Family Perceived Support Questionnaire (ICE-FPSQ)
Bibliographic record
Abstract
Valid and reliable instruments are needed to measure how family members perceive support from nurses when a family member is experiencing serious illness. The purpose of this article is to describe the development and psychometric testing of a new instrument, the Iceland-Family Perceived Support Questionnaire (ICE-FPSQ). The concepts in the original version of the ICE-FPSQ (suggesting 24 items and 4 categories) were developed from the Calgary Family Intervention Model. In the first phase of the instrument construction, 179 family members answered the original ICE-FPSQ, and 236 answered the questionnaire in the second phase of testing. Principal Component Analysis (PCA) reduced the original questionnaire to 21 items. Cronbach's α = .959 explained 68% of the total variance, with three factors emerging: (a) emotional support (α = .925), (b) recognition of families' strengths (α = .926), and (c) cognitive support (α = .841). Confirmatory Factor Analyses (CFA) resulted in a final version of the questionnaire containing 14 items with total alpha of .961 and two factors: (a) cognitive support (α = .881) and (b) emotional support (α = .952). The instrument measures family's perceptions of support provided by nurses and will be helpful in examining the usefulness of family nursing interventions.
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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".