Digital photography as a pedagogical learning approach in nursing education
Bibliographic record
Abstract
Background and objective: The traditional teaching methods in practical nursing education seem to be either too theoretical or too practical. Therefore, combinations of theoretical and new pedagogical approaches are needed to teach and train students in practical nursing. The objective of this study was to explore the nursing students' experiences with the use of photography in the Simulation Unit, and to describe how photographs affect development of practical skills.Methods: A descriptive and interpretive qualitative design was used. The collection of data was conducted by asking fifty-four students to answer three questions online after each session and based on the day’s photographs. All photographs taken by the groups were uploaded to a virtual meeting room, a total of eight hundred and fifty-five photos. The photographs formed the basis for determining what the students have learned from examining and exploring photographs of the students themselves and their peers. Their subjective statements and experience were later downloaded and analysed using phenomeological analysis.Results: Photography positively influences the nursing students’ learning, increased their self-confidence and enhances competence in the exercise of skills. This gave the students a deeper understanding of the complexity of the practical procedures based on knowledge-based practice, and they learned quickly and easily.Conclusions: The students become more self-regulated learners, developed better self-confidence, and bolstered their learning competence in relation to the degree requirements for knowledge-based procedural learning. There was consensus among the students that the photographs were a useful learning tool, both intra- and interactive, and were a useful supplement in learning practical skills.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".