Efficacy of the Use of the Calgary Family Intervention Model in Bedside Nursing Education: A Systematic Review
Bibliographic record
Abstract
Objective: To objectively analyze the research for empirical evidence of the efficacy of the use of the Calgary Family Intervention Model (CFIM) in assisting bedside education by nurses and to identify facilitators and barriers to the use of the Model. Methods: Four research databases (PubMed [MEDLINE], CINAHL, Web of Science, and Science Direct) were queried for studies commensurate with the objective statement from 1990 to 2021. In total, 169 articles were initially identified in the search, 135 were screened after duplicates and ineligible articles were removed, ultimately leaving the sample of 24 articles for the review. Results: There is significant evidence to conclude that the CFIM is a very useful model to be used by nurses for bedside education and to improve overall patient and family outcomes. It enables communication, collaboration, and therapeutic conversations. The use of CFIM by nurses serves as a resource for both them and families and patients involved. There are some concerns to the use of CFIM as there are family dynamic issues, which result in problems providing care to patients. A lack of family sharing can result in inadequate care to the patient as well as unrealistic expectations from family members involved. Conclusion: The CFIM is an excellent tool to enable nurses to provide education at the bedside and to enable improved patient and family outcomes. The use of the tool is suggested in situations where it would improve the level of care provided to patients and families.
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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 | 0.019 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".