Navigating difficult conversations: the role of self‐monitoring and reflection‐in‐action
Bibliographic record
Abstract
CONTEXT: Clinicians draw upon experiential knowledge to manoeuvre difficult conversations, using tacit knowledge that is difficult to explicitly teach. Instead, learners are taught to communicate through role-play and checklists, both of which are approaches that may fail during moments of clinical complexity. We know that difficult conversations may provoke anxiety in learners, but we know little about how they learn to navigate them. Without a deeper understanding, we may fail to equip learners with the skills to manoeuvre these conversations in practice. METHODS: Using constructivist grounded theory, we applied the sensitising concepts of self-monitoring and reflection-in-action both to explore the process in which trainees engage to navigate difficult conversations and to expand understanding about these theories. We situated our research in the neonatal intensive care unit (NICU), in which difficult conversations are ubiquitous. Fifteen resident and fellow trainees drew rich pictures about difficult conversations, and shared their drawings and experiences during semi-structured interviews. Interview transcripts were analysed using constant comparative analysis. RESULTS: Participants described how they responded when checklist approaches became ineffective during moments of unexpected uncertainty and complexity. For participants, these indeterminate zones of practice triggered a process of seeing families differently and then pausing to understand problems that arose with the checklist-based approach. Throughout this process, learners actively observed others' communication approaches, negotiated their roles within difficult conversations, and abandoned the checklist to engage differently with families. CONCLUSIONS: Our findings suggest links between the theories of self-monitoring and reflection-in-action, and describe the engagement of both processes in the context of NICU conversations. Self-monitoring may lead to the realisation of an indeterminate zone of practice, after which trainees may respond through reflection-in-action. We recognise that training programmes may need to teach a checklist-based approach as a starting point. We suggest that trainees also be given purposeful opportunities and support to depart from checklists in order to compassionately and flexibly navigate difficult conversations with families.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".