Leading for the long haul: a mixed-method evaluation of the Sustainment Leadership Scale (SLS)
Bibliographic record
Abstract
BACKGROUND: Despite our progress in understanding the organizational context for implementation and specifically the role of leadership in implementation, its role in sustainment has received little attention. This paper took a mixed-method approach to examine leadership during the sustainment phase of the Exploration, Preparation, Implementation, Sustainment (EPIS) framework. Utilizing the Implementation Leadership Scale as a foundation, we sought to develop a short, practical measure of sustainment leadership that can be used for both applied and research purposes. METHODS: Data for this study were collected as a part of a larger mixed-method study of evidence-based intervention, SafeCare®, sustainment. Quantitative data were collected from 157 providers using web-based surveys. Confirmatory factor analysis was used to examine the factor structure of the Sustainment Leadership Scale (SLS). Qualitative data were collected from 95 providers who participated in one of 15 focus groups. A framework approach guided qualitative data analysis. Mixed-method integration was also utilized to examine convergence of quantitative and qualitative findings. RESULTS: Confirmatory factor analysis supported the a priori higher order factor structure of the SLS with subscales indicating a single higher order sustainment leadership factor. The SLS demonstrated excellent internal consistency reliability. Qualitative analyses offered support for the dimensions of sustainment leadership captured by the quantitative measure, in addition to uncovering a fifth possible factor, available leadership. CONCLUSIONS: This study found qualitative and quantitative support for the pragmatic SLS measure. The SLS can be used for assessing leadership of first-level leaders to understand how staff perceive leadership during sustainment and to suggest areas where leaders could direct more attention in order to increase the likelihood that EBIs are institutionalized into the normal functioning of the organization.
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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 | 0.086 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".