Gut feeling: A grounded theory study to identify clinical educators' reasoning processes in putting students on a learning contract
Bibliographic record
Abstract
AIM: To develop a substantive theoretical explanation that makes sense of the decision-making process that clinical instructors use to place students on a learning contract. BACKGROUND: Clinical instructors are challenged with the task of objectively evaluating students using subjective tools such as anecdotal notes, diaries, unstructured observations and verbal feedback from other nurses. Clinical instructors' assessment decisions have a considerable impact on a variety of key stakeholders, not least of all students. DESIGN: Grounded theory method and its heuristic tools including the logic of constant comparison, continuous memoing and theoretical sampling to serve conceptualisation were used in the process of data collection and analysis. METHODS: Seventeen individual semi-structured interviews with clinical instructors in one university in Western Canada were conducted between May 2016-May 2017. Data were analysed using open, axial and selective coding consistent with grounded theory methodology. The study was checked for the Standards for Reporting Qualitative Research (SRQR) criteria (See Appendix S1). FINDINGS: Three subcategories, "brewing trouble," "unpacking thinking" and "benchmarking" led to the study's substantive theoretical explanation. "Gut feeling" demonstrates how clinical instructors reason in their decision-making process to place a student on a learning contract. CONCLUSION: Placing a student on a learning contract is impacted by personal, professional and institutional variables that together shift the process of evaluation towards subjectivity, thus influencing students' competency. A system-level approach, focusing on positive change through implementing innovative assessment strategies, such as using a smart phone application, is needed to provide some degree of consistency and objectivity. RELEVANCE TO CLINICAL PRACTICE: Making visible the objective assessments currently being done by clinical instructors has the potential to change organisational standards, which in turn impact patient and clinical outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".