Engaging health students in family assessment through the use of a simulated video as a blended learning resource
Bibliographic record
Abstract
Background: Blended learning resources increase student engagement and contribute to their understanding of theory to practice by creating a flexible and student friendly delivery. Simulation of ‘real life nursing’ enables equitable learning across student groups. The development of four family assessment videos demonstrating therapeutic communication and family assessment provided a learning resource for health students. Guided by the Family Systems Theory which identifies the patient and family as a unit, this presentation will outline the development and evaluation of family assessment videos. Theory: The family assessment videos are based on the Australian Family Strengths Nursing Assessment Guide [AFSNAG] and the Calgary Family Assessment Model [CFAM]. Using best practice guidelines for development of blended learning resources the videos were developed from case studies, into scripts and then simulations. A continuous review process over the development was conducted to ensure quality and authenticity. Actors were used as family members to ensure ethical standards were not compromised during the production of the videos. Outcomes: Case studies were developed using a team approach, previous clinical experience, qualitative research and developed course work to ensure validity to the family situations. Questions from family assessment models were integrated into the scripts to prompt students to identify links between family nursing theory and practice. Each case study and subsequent script were reviewed by experienced academics, particularly the indigenous case study to ensure appropriate depiction of the family scenario. Conclusions: The development of the family assessment videos as a blended learning resource used a multifaceted approach to reflect authenticity and promote student engagement. Using best practice guidelines for blended learning resources ensured clarity and appropriate length. The project was guided by Family Systems Theory which enabled a focus on family as a unit of care, providing a rich resource for health students in a variety of courses.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".