Learning nursing during the COVID-19 pandemic: The importance of perceived relatedness with teachers and sense of coherence
Bibliographic record
Abstract
Background and objective: In March 2020, the COVID-19 pandemic infected populations worldwide. To limit the spread, many countries declared stay-at-home orders. Teachers were suddenly obliged to teach and facilitate learning online, whereas students had to manage online education alone from home. Within self-determination theory (SDT), the need for relatedness is considered crucial for personal growth, well-being, motivation and learning, whereas sense of coherence (SOC) is a salutogenic health concept explaining humans’ coping with stressful situations. The aim of this study was to investigate the importance of teacher relatedness as well as SOC, including the concepts of comprehensibility, manageability and meaningfulness, among nursing students during the COVID-19 pandemic.Methods: Survey data were collected from 329 nursing students at a large university in Norway. Twelve hypotheses of the associations between teacher relatedness, SOC and perceived learning were tested by means of structural equation modelling (SEM) using Stata.Results: The SEM yielded an acceptable fit (χ2 = 177.60, p = .000, df = 80, χ2/df = 2.22, RMSEA 0.063, CFI = 0.96, SRMR = 0.048), showing significant, positive relationships between the latent variables of teacher relatedness, SOC and perceived learning. Eleven out of the twelve hypotheses found support, showing both direct and indirect relationships between the latent variables.Conclusions: The study indicates that caring and close teachers seem very important for nursing students’ learning during the COVID-19 pandemic. Many students do not experience such a teacher relationship, resulting in difficulties studying and learning under crisis. Knowledge about how teachers may mobilize care and close relationships with nursing students is important for students’ learning during a pandemic situation when teaching go digital.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".