Integrating quality of life assessments in student clinical learning experience
Bibliographic record
Abstract
Healthcare organizations and governments increasingly emphasize the importance of viewing patients and families as equal members of the care team, with the right to participate in decisions affecting their care. In order to support the imperatives of person-centred care, Quality of Life (QOL) assessments are utilized more as part of routine clinical care. QOL assessments involve the use of standardized, validated questionnaires completed by patients to assess their health and quality of life from their own point of view. In an effort to support transformational learning about QOL assessments, fourth-year students in a BSN program completed an assignment that involved using two QOL assessment instruments as part of a course on Nursing Care of Complex Illness; the McGill Quality of Life Revised instrument (MQOL-R) and the Edmonton Symptom Assessment System Revised (ESAS-R). Each student invited a patient with life-limiting illness to complete the two instruments, discuss the results with their patient, and identify potential interventions that would address the priorities that correspond with patient-identified areas of concern. They were then required to write a reflective paper on their experience. Analysis of the students’ reflections was guided by the qualitative methodology of interpretive description. The following six thematic patterns were identified: (a) student expectations and patient responses, (b) comfort level using QOL assessment instruments, (c) therapeutic person-centred communication, (d) putting the patient first – prioritizing care from the patients’ point of view, (e) insight into the lived experience of patients, and (f) use of nurses’ time. This study provides preliminary guidance based on student perspectives regarding the inclusion of QOL assessments in nursing education.
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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".