Examining health care providers’ and middle-level managers’ readiness for change: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Readiness is a critical precursor of successful change; it denotes whether those involved in the change are motivated and empowered to participate in the change. Research on readiness tends to focus on frontline providers or individuals in non-managerial positions and offers limited attention to individuals in middle management positions who are expected to lead frontline providers in change implementation. Yet middle-level managers are also recipients of changes that are planned and decreed by those in higher positions. This study sought to examine both frontline provider and middle manager readiness for change in the context of primary care program integration. METHODS: Using a qualitative case study approach, we examined how frontline providers and middle managers experienced six readiness factors: discrepancy, appropriateness, valence, efficacy, fairness and trust in management. Data were collected through documents, meeting observation and semi-structured interviews with frontline providers and middle managers involved in the change. RESULTS: The findings highlighted similarities and differences in readiness experiences of frontline providers and middle managers. Across both types of participants, we found that the notion of valence should be expanded to consider individuals' evaluation of benefits to patients and the health system; efficacy applies to both content and process of change; fairness and trust in management findings require further exploration to determine their appropriateness to be incorporated in models of readiness for change; and trust in management (or lack of trust) has a cascading influence across the levels in the organization. CONCLUSIONS: Our study makes a contribution by nuancing and extending conceptualizations of individual readiness factors, and by highlighting the central role of middle manager readiness for change. Implications of the study include the need to consider readiness factors prior to the implementation of change and the importance of fostering readiness throughout all levels of the organization.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".