Information technology implementation in service enhancement: a qualitative case study
Bibliographic record
Abstract
The article presents a qualitative case study of the complexities involved in information technology (IT) implementation through illuminating the methods used by two different nurses in the same service enhancement initiative. The importance, relevance and history of learning IT skills is introduced and qualitative case study is argued to be a suitable methodology for investigating and unlocking such social phenomena. Three interviews were conducted and the data combined into a single narrative. One of the participants is very familiar with IT yet reverts to paper for managing numerical data arising from a waiting times audit. This project is taken over by a second nurse who immediately replaces the paper method with a spreadsheet, with help from a service enhancement champion, the third individual interviewed. The circumstances and implications of the nurses’ decisions, to avoid or deploy IT, are briefly discussed. The paper concludes that it is unrealistic to expect all nurses to be extremely fluent with IT. However, the decision to leverage IT for innovation and improvement in clinical settings is made easier if education has provided nurses with a firm conceptual and practical foundation.
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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".