Forming professional identities on the health care team: discursive constructions of the ‘other’ in the operating room
Bibliographic record
Abstract
BACKGROUND: Inter-professional health care teams represent the nucleus of both patient care and the clinical education of novices. Both activities depend upon the'talk' that team members use to interact with one another. This study explored team members' interpretations of tense team communications in the operating room (OR). METHODS: The study was conducted using 52 team members divided into 14 focus groups. Team members comprised 13 surgeons, 19 nurses, nine anaesthetists and 11 trainees. Both uni-disciplinary (n = 11) and multi-disciplinary (n = 3) formats were employed. All groups discussed three communication scenarios, derived from prior ethnographic research. Discussions were audio-recorded and transcribed. Using a grounded theory approach, three researchers individually analysed sample transcripts, after which group discussions were held to resolve discrepancies and confirm a coding structure. Using the confirmed code, the complete data set was coded using the 'NVivo' qualitative data analysis software program. RESULTS: There were substantial differences in surgeons', nurses', anaesthetists', and trainees' interpretations of the communication scenarios. Interpretations were accompanied by subjects' depictions of disciplinary roles on the team. Subjects' constructions of other professions' roles, values and motivations were often dissonant with those professions' constructions of themselves. CONCLUSIONS: Team members, particularly novices, tend to simplify and distort others' roles and motivations as they interpret tense communication. We suggest that such simplifications may be rhetorical, reflecting professional rivalries on the OR team. In addition, we theorise that novices' echoing of role simplification has implications for their professional identity formation.
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 | 0.031 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.057 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| 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".