An Exploration of Organizational Characteristics and Training Adoption in Irish Community Drug Treatment Services
Bibliographic record
Abstract
BACKGROUND: Changes in patterns of drug use and population needs necessitate the adoption of new technologies. Despite high failure rates in adopting new technologies acquired in training, little is known about the process that can support successful change. This study explores the impact that staff and service characteristics have on the process of training adoption in Irish opiate substitution therapy services, with a specific focus on the concept of organizational readiness to change. METHODS: A cross-sectional survey was conducted on a convenience sample of 132 staff members across 12 services in Ireland. The relationship between staff demographics, their perceptions of organizational readiness to change, burnout, and a four-stage process of training adoption were considered. RESULTS: Discipline, job tenure, and educational levels are important predictors of engagement in the adoption process. Staff in services with higher institutional needs, greater pressures for change, and poorer resources were less likely to be exposed to, or adopt, training. Having lower levels of stress and more influence with peers was associated with better adoption of training. CONCLUSIONS: Planners and service managers need to carefully consider the composition or dynamics of services when initiating change. Organizational readiness to change and staff characteristics as measured by instruments used in this study are important determinants of the process of innovation or training adoption and provide a good basis for developing further understanding of how treatment services work. This article expands on results from previous studies conducted in the United States to a European context.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".