Student’s Inventory of Professionalism (SIP): A Tool to Assess Attitudes towards Professional Development Based on Palliative Care Undergraduate Education
Bibliographic record
Abstract
Introduction: Quality medical education, centered on a patient’s needs, is crucial to develop the health professionals that our society requires. Research suggests a strong contribution of palliative care education to professionalism. The aim of this study was to design and validate a self-report inventory to measure student’s professional development. Method: Sequential exploratory strategy mixed method. The inventory is built based on the themes that emerged from the analysis of four qualitative studies about nursing and medical students’ perceptions related to palliative care teaching interventions (see Ballesteros et al. 2014, Centeno et al. 2014 and 2017, Rojí et al. 2017). The structure and psychometrics of the inventory obtained is tested in two different surveys with two different groups of medical students. Inventory reliability and construct validity are tested in the first survey group. To verify the inventory structure, a confirmatory factor analysis is performed in a second survey group. Results: The inventory has 33 items and seven dimensions: a holistic approach, caring for and understanding the patient, personal growth, teamwork, decision-making, patient evaluation, and being a health care professional. Cronbach’s-alpha was 0.73–0.84 in all seven domains, ICC: 0.95. The confirmatory factor analysis comparative fit index (CFI) was 1 with a standardized root mean square Index 0.088 (SRMR) and obtained a 0.99 goodness-of-fit R-square coefficient. Conclusions: this new inventory is grounded on student’s palliative care teaching experiences and seems to be valid to assess student’s professional development.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".