A narrative inquiry into the experience of a practical nursing graduate with private tutoring
Bibliographic record
Abstract
Disability studies are an established area of scholarship in education and this has been my passion for the last decade. Students living with disabilities in healthcare profession education (including nursing, personal support, occupational and physical therapy), as well as the use of professional tutors as an educational support among this population, is under researched. Throughout my practice as a professional health sciences tutor, I have wondered how this population of students experiences both one to one tutoring over time and educational accommodations. In this Narrative Inquiry (Clandinin & Connelly, 2000), my co-participant and I go on a journey together to explore how a graduate from an Ontario Registered Practical Nursing program with a diagnosed disability impacting her learning experienced individualized tutoring. Through a series of five semi-structured narrative interviews and self-reflection, the co-participant???s story was re-constructed and analyzed using the Narrative Inquiry three-dimensional space (temporality, sociality, and place). This Narrative Inquiry highlights the temporal connections of life events and how social conditions mutually shape and change personal conditions. Four narrative threads: barriers to access, stigmatization, individuality in education, and paradoxical conflicts among caring professions. It also highlights the importance of reflective practice, barrier-free inclusive education, and the need for further research into tutoring practice, education and policy. Tutors must establish a relationship that allows for exploration of context and building of a contextually-meaningful program, one in which learning is fostered.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| 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".