"I should have discharged him but I felt guilty": a qualitative investigation of clinicians' emotions in the context of implementing occupational therapy
Bibliographic record
Abstract
BACKGROUND: Clinicians' emotions about practice are a potentially powerful yet largely overlooked factor in implementation of good-quality care. The present paper expands the current, limited evidence about clinicians' emotions by (i) describing clinician-reported examples of emotions about practice and (ii) identifying the clinical situations in which, according to clinicians, emotions emerge and influence practice. METHODS: Semi-structured, face-to-face interviews with 25 clinicians (children's occupational therapists) were conducted across six health care organisations. Participants were asked to reflect on their practice in two recent patient cases, one that they perceived 'successful' and another 'unsuccessful'. Interviews were transcribed verbatim, and the transcripts were analysed for emerging themes. A proportion of transcripts were independently read and coded, and the themes were validated through critical discussion. RESULTS: A key theme was clinicians' emotions, especially negative emotions including guilt, anger, worry, frustration and inadequacy. These were described in connection with situations where the clinicians perceived that (i) they failed to provide good quality care, (ii) they were unable to achieve positive health outcomes or engage the patient or (iii) there was conflict between what they were asked to do and the norms they held important. CONCLUSIONS: Clinicians experience a range of negative emotions about practice. These are particularly likely to emerge in situations where clinicians perceive that their actions and practice fall short of the standards, norms or outcomes that they hold as important. The results inform the specification of emotions and emotion-triggering situations for future investigations of health care implementation.
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 | 0.021 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".