Crossing the “Ascent to Competence” Borders Into Privileged Belongingness Space: Practical Nursing Students’ Experiences in Clinical Practice
Bibliographic record
Abstract
BACKGROUND: There is substantial evidence that students' sense of belonging in clinical practice influence their motivation, learning, and acquisition of skills. Despite the importance of belongingness in clinical education, not many studies have been done in the Canadian context in general and practical nurse education in particular. PURPOSE: The main objective of this study was to explore practical nursing students' experiences in clinical education with regard to their sense of belongingness. In addition, we also examined clinical instructors' experiences in fostering a sense of belongingness to students in clinical settings. METHODS: Ascent to competence conceptual framework was used as an analytic lens in this study. Students enrolled in a two-year diploma in practical nursing and their instructors participated in the study. A total of 12 students and 4 clinical instructors participated in the in-depth semistructured one-on-one interviews. Inductive and deductive thematic analysis was used to analyze the data. Deductive analysis used predetermined themes as per ascent to competence framework. Inductive thematic analysis allowed us to provide additional themes on concepts that could not be effectively explained by the predetermined themes. RESULTS: Students described the uniqueness of their instructors and clinical sites as important attributes that enable them to cross various "borders" as they gain access to a "belongingness space" of acceptance, respect, and competency. Productive learning experiences were achieved by students who felt supported and "granted permission" to enter the belongingness space. The instructors highlighted their perceived roles in providing scaffolding supports they thought would help students feel a sense of belonging. CONCLUSIONS: Students cross several borders as they persevere to gain access to the revered belongingness space. Clinical instructors play a major role in assisting students to enter the belongingness space where meaningful learning occurs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".