Nurses’ fidelity to theory‐based core components when implementing Family Health Conversations – a qualitative inquiry
Bibliographic record
Abstract
BACKGROUND AND AIM: A family systems nursing intervention, Family Health Conversation, has been developed in Sweden by adapting the Calgary Family Assessment and Intervention Models and the Illness Beliefs Model. The intervention has several theoretical assumptions, and one way translate the theory into practice is to identify core components. This may produce higher levels of fidelity to the intervention. Besides information about how to implement an intervention in accordance to how it was developed, evaluating whether it was actually implemented as intended is important. Accordingly, we describe the nurses' fidelity to the identified core components of Family Health Conversation. INTERVENTION AND RESEARCH METHODS: Six nurses, working in alternating pairs, conducted Family Health Conversations with seven families in which a family member younger than 65 had suffered a stroke. The intervention contained a series of three-1-hour conversations held at 2-3 week intervals. The nurses followed a conversation structure based on 12 core components identified from theoretical assumptions. The transcripts of the 21 conversations were analysed using manifest qualitative content analysis with a deductive approach. RESULTS AND CONCLUSION: The 'core components' seemed to be useful even if nurses' fidelity varied among the core components. Some components were followed relatively well, but others were not. This indicates that the process for achieving fidelity to the intervention can be improved, and that it is necessary for nurses to continually learn theory and to practise family systems nursing. We suggest this can be accomplished through reflections, role play and training on the core components. Furthermore, as in this study, joint reflections on how the core components have been implemented can lead to deeper understanding and knowledge of how Family Health Conversation can be delivered as intended.
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".