Advantages of and Barriers to Crafting New Technology in Healthcare Organizations: A Qualitative Study in the COVID-19 Context
Bibliographic record
Abstract
Nursing professionals are constantly required to adapt to technological changes, and especially so in the wake of COVID-19, which has prompted the development of new digital tools. A new and specific form of job crafting in relation to new technology has recently emerged in the literature; that is, adoption job crafting. However, little is known about this specific form of job crafting, especially within the pandemic context. We aim, in this study, to explore the advantages of and barriers to adoption job crafting. We used NVivo software to analyze 42 semi-structured interviews conducted during COVID-19. Our findings revealed that nurses had proactive and positive attitudes toward new technology (adoption job crafting) to enhance efficiency, sustainability, well-being, virtual teamwork, communication, and knowledge sharing. We also identified many barriers to adoption job crafting due to several organizational obstacles, such as the lack of human resource management practices, especially training, and the characteristics of the technology used. We contribute to the literature by documenting innovative cases of and barriers to adoption job crafting, which have not been explored before. These findings stress the necessity to adopt human resources practices, especially training, to foster positive job crafting among nurses and safeguard their adaptive expertise.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".