Implementation of person-centred care: management perspective
Bibliographic record
Abstract
Objective: In this study the implementation of a care model was examined in a public hospital in Sweden. The aim was to identify, from the management perspective, barriers and facilitators with respect to the implementation. A further aim was to study the explanatory power of a theoretical framework, normalization process theory (NPT). Method: Semi-structured interviews were conducted with all of the members of a hospital departments’ managerial group. Interview transcripts were analysed by means of directed deductive content analysis, applying NPT as theoretical frame work. Results: The respondents identified factors, which were perceived as facilitating or obstructing the implementation process. These factors were; organizational culture, distribution of power, patient characteristics, resistance to change, teamwork, efficiency, time and speed of implementation. The theoretical framework, NPT, was partly supported by the data. There was however an absence of collective action and reflexive monitoring constructs. Conclusion: The implementation process, according to NPT, was incomplete and there was a risk that it could regress to the previous work routines. However, implementation theories, including NPT, do not have a timeframe for the implementation process. Even though theories are able of describing in detail the steps for successfully embedding and sustaining an innovation, they do not describe or identify factors influencing the speed of the implementation. A possible reason might be that time is a subjective factor.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".