35 Catalysing nurse middle managers clinical leadership development through peer-to-peer shadowing: start tomorrow!
Bibliographic record
Abstract
Purpose The purpose of this study was to explore the experiences and impact of peer-to-peer shadowing as a technique to develop nurse middle managers’ clinical leadership practices. Design/Methodology/Approach A qualitative descriptive study was conducted to gain insight into the experiences of nurse middle managers using semi-structured interviews. Data were analysed into codes using constant comparison and similar codes were grouped under sub-themes and then into four broader themes. Findings Peer-to-peer shadowing facilitates collective reflection-in-action and enhances an ‘investigate stance’ while acting. Nurse middle managers begin to curb the caring disposition that unreflectively urges them to act, to answer the call for help in the here and now, focus on ad hoc ‘doings’, and make quick judgements. Seeing a shadowee act produces, via a process of social comparison, a behavioural repertoire of postponing reactions and refraining from judging. Balancing the act of stepping in and doing something or just observing as well as giving or withholding feedback are important practices that are difficult to develop. Originality/Value Peer-to-peer shadowing facilitates curbing the caring disposition, which is essential for clinical leadership development through unlocking a behavioural repertoire that is not easy to reveal because it is, unreflectively, closely knit to the professional background of the nurse managers. Unlike most leadership development programmes, that are quite introspective and detached from context, peer-to-peer shadowing does have the potential to promote collective learning while acting, which is an important process.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".