HUR EN ORGANISATION KAN ÖKA FÖRUTSÄTTNINGARNA FÖR EN LYCKAD IMPLEMENTATION AV IT. : En kvalitativ studie om hur en offentlig verksamhet kan arbeta med att öka förutsättningarna för en lyckad implementation av IT i en förändringsprocess.
Bibliographic record
Abstract
Organizations are being more and more dependent on digital platforms and intranets to be as efficient as they can. Digitizing is enabling different ways to streamline operations and make them more effective. The public sector is no different. But due to different factors they have not been able to keep up with the surrounding tempo regarding digitizing. Healthcare in the public sector is nowadays continuously being digitized with new platforms and intranets to use internally and systems for the patients to use externally. To be able to tackle the challenge with digitizing healthcare in the public sector that has not previously caught up with all the new technology and IT-systems they need to be able to lead change, whereof change management is getting more and more relevant. But it is not just change management that is getting relevant, it is change management with relation to IT, a previously inadequate research area maybe due to the fact that it is so specific or has not been around long enough. The aim of this study is to research how a region is currently working with communication, IT and change management to be able to examine how they can create a better collective understanding internally during implementation of IT with the goals of creating a understanding for how the many different aspects in play can increase the conditions, with the end goal of a successful implementation in healthcare within the public sector.
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 | 0.020 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".