Impact of Embedded Remediation in Literacy Skills for First Semester Practical Nursing Students in one Ontario College Program
Bibliographic record
Abstract
The purpose of this research study was to explore whether first semester Practical Nursing (PN) students who received embedded remediation performed better academically than their colleagues who did not, and the relationship between those students’ demographic characteristics and their performance in the intervention. More specifically, the study examined the relationships between the PN students’ communication skill level and academic performances as reflected in their performance in two core nursing courses and end of semester retention rates. This was a post ex-facto intervention case study of Urban College (pseudonym). Forty-eight students participated for a response rate of 42%. Cohort I consisted of 25 participants (11 remedial and 14 non-remedial) and Cohort II included 23 participants (12 at-risk and 11 non-remedial). It compared the performance of the experimental group which had experienced a remedial intervention with that of the at-risk control group that had not experienced the intervention. Demographic characteristics of these two groups were compared with those of their colleagues who had been designated as non-remedial. The quantitative data analyzed supported the main finding that participation in embedded remediation had a positive impact on the retention of participating students. The qualitative data captured the perceptions of these students and the themes that emerged maintain that academic and social integration is important to student attainment. The findings support the theoretical frameworks that grounded this study: Tinto’s Student Integration Model and Learning Communities in Higher Education. The findings in this study have implications for practice, policy and further research aimed at improving the success rate of those students who enter PN programs with inadequate communication skills. Addressing these issues is crucial so that the learning needs of all students are met; particularly those preparing to enter the health care field where communication skills are critical for providing safe patient care. College of Nurses of Ontario applicants are expected to be proficient in the four language skills of writing, reading, speaking and listening. Although the findings of this case study are not generalizable, I hope the findings will be of interest to other educators wanting to assist underprepared post-secondary students struggling with communication challenges.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".