Professional conceptualisation and accomplishment of patient safety in mental healthcare: an ethnographic approach
Bibliographic record
Abstract
BACKGROUND: This study seeks to broaden current understandings of what patient safety means in mental healthcare and how it is accomplished. We propose a qualitative observational study of how safety is produced or not produced in the complex context of everyday professional mental health practice. Such an approach intentionally contrasts with much patient safety research which assumes that safety is achieved and improved through top-down policy directives. We seek instead to understand and articulate the connections and dynamic interactions between people, materials, and organisational, legal, moral, professional and historical safety imperatives as they come together at particular times and places to perform safe or unsafe practice. As such we advocate an understanding of patient safety 'from the ground up'. METHODS/DESIGN: The proposed project employs a six-phase data collection framework in two mental health settings: an inpatient unit and a community team. The first four phases comprise multiple modes of focussed, unobtrusive observation of professionals at work, to enable us to trace the conceptualisation and enactment of safety as revealed in dialogue and narrative, use of artefacts and space, bodily activity and patterns of movement, and in the accomplishment of specific work tasks. An interview phase and a social network analysis phase will subsequently be conducted to offer comparative perspectives on the observational data. This multi-modal and holistic approach to studying patient safety will complement existing research, which is dominated by instrumentalist approaches to discovering factors contributing to error, or developing interventions to prevent or manage adverse events. DISCUSSION: This ethnographic research framework, informed by the principles of practice theories and in particular actor-network ideas, provides a tool to aid the understanding of patient safety in mental healthcare. The approach is novel in that it seeks to articulate an 'anatomy of patient safety' as it actually occurs, in terms of the networks of elements coalescing to enable the conceptual and material performance of safety in mental health settings. By looking at how patient safety happens or does not happen, this study will enable us to better understand how we might in future productively tackle its improvement.
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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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".