The Effect of Mood on Medical Students' Diagnostic Performance
Bibliographic record
Abstract
It is clear that mood and emotion play an important role in how people deal with problems [1]. The problem-solving strategies adopted by people when they are in a happy mood are quite different than the strategies used by people when they are in a sad mood [1]. It is also well documented in the literature that individuals’ affective feelings influence their evaluative judgment, strategies of information processing, and the type of information retrieved from memory [1]. One area where this knowledge has important implications is in the design, application, and effectiveness of cognitive tools. Cognitive tools are tools that “help students during thinking, problem solving, or learning by providing them with opportunities to practice applying their knowledge in the context of complex, meaningful activities rather than in isolation of their ultimate use” (p. 88) [2]. A computer-based learning environment (CBLE) incorporating a number of cognitive tools of particular interest for this paper is called BioWorld [2]. BioWorld provides medical students with instruction, model proficiency, and an assessment of their knowledge in a more authentic scenario than standard classroom learning [2]. These tools have been shown to effectively promote scientific reasoning in high school students [3], and have been adapted for use with medical students. We propose replicating the emotional side of working in the medical field by manipulating the affect of students before they engage in a BioWorld problem. Addressing the relationship between mood and doctors’ performance is extremely important, especially with researchers suggesting that these types of emotions are normal, inevitable, and capable of negatively effecting patient care [4].
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".