Bibliographic record
Abstract
Purpose: Significant time and effort are needed to facilitate organizational change; thus a well-constructed conceptual model may help health professionals identify and overcome the barriers impeding this process.Design / methodology / approach:Currently, there is no single framework for organizational change that has gained widespread acceptance. However, two well-validated organizational models are the Prochaska and DiClemente transtheoretical model and Green et al. al.’s health promotion model. In this paper we synthesize these models in the context of organizational change for a physician audience. Findings: We created a new model of organizational change that keeps the best elements of both the Prochaska and DiClemente transtheoretical model and Green et al. al.’s health promotion model. Furthermore, an example is illustrated using this approach.Originality / value: Most health organizations lack a consistent approach to managing change. As a result they have not been as effective in this area as they could be. Most previous organization change theorists have attempted to solve this dilemma by constructing new models of organizational change, which they hope will eventually become the dominant model. Our approach is original in that we have incorporated the best features of two pre-existing models. The value of this approach is that improving these existing models has a much greater potential for widespread acceptance than developing yet another new model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".