Voiced inner dialogue as relational reflection-on-action: The case of middle managers in health care
Bibliographic record
Abstract
We look to the experiences of middle managers in a health-care setting to empirically develop and explore the concept of voiced inner dialogue. Voiced inner dialogue is conceptualised as a form of reflection-on-action whereby fragments of narrative self-reflection reveal an organisation’s unspoken backdrop conversation or interpersonal mush. The normalised intensity that characterises many health-care settings, an artefact of increased governmentality and responsibilisation, leaves middle managers experiencing increased work and personal pressures. The interpersonal mush in this context is centred upon individuals’ felt disconnect between espoused and enacted organisational values. Voiced inner dialogue was triggered in dialogic conversation with the researchers, a type of participant-focused reflexivity. From our qualitative analysis, we present three themes to illuminate how organisational context can inform the creation and maintenance of interpersonal mush, impeding managers’ reflection. Voiced inner dialogue offers an opportunity for managers stuck in the silence of interpersonal mush to engage in reflection-on-action. We conclude with the implications for reflection, reflexivity and management learning.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.038 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".