Coaching to develop leadership for healthcare managers: a mixed-method systematic review protocol
Bibliographic record
Abstract
BACKGROUND: An increasing number of interventions have focused on leadership development for healthcare managers, among which coaching is a common strategy. The purpose of the present systematic review is to synthesize evidence on the effect of coaching in developing leadership of healthcare managers. METHODS AND ANALYSIS: A literature search will be conducted in six English databases (MEDLINE (Ovid), CINAHL, Embase, Cochrane library, Nursing & Allied Health Premium, and Scopus) and four Chinese databases (Wanfang, CNKI, SinoMed, and VIP) from inception to April 1st, 2022. The titles, abstracts, and full texts of the studies will be screened by two independent researchers to determine their eligibility. The RoB 2, ROBINS-I, CASP, and MMAT will be applied to assess the quality of randomized trials, non-randomized studies, qualitative studies, and mixed-method studies, respectively. We will then extract the study characteristics, participant characteristics, and study outcomes of the reviewed papers. The Aims, Ingredients, Mechanism, and Delivery framework will be used to extract the components of coaching strategies. For quantitative data, a meta-analysis will be performed if sufficient data are available; otherwise, we will conduct a narrative synthesis. Thematic synthesis methods will be used for qualitative data analysis. DISCUSSION: By conducting this systematic review, we expect to synthesize evidence regarding the components of coaching for leadership development among healthcare managers; the influence of coaching on leadership development among managers at the individual, unit-wide, or organizational level; and how managers view coaching as a leadership development strategy. TRIAL REGISTRATION: PROSPERO registration number: CRD42020194290 .
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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 | 0.141 | 0.096 |
| Meta-epidemiology (narrow) | 0.007 | 0.008 |
| Meta-epidemiology (broad) | 0.020 | 0.017 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.073 | 0.012 |
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".