Bibliographic record
Abstract
AIM: The aim of this scoping literature review was to examine and summarize the factors, context, and processes that influence work motivation of health care workers. METHODS: A scoping literature review was done to answer the question: What is known from the existing empirical literature about factors, context, and processes that influence work motivation of health care workers? This scoping review used the Arksey and O'Malley framework to describe and summarize findings. Inclusion and exclusion criteria were developed to screen studies. Relevant studies published between January 2005 and May 2016 were identified using five electronic databases. Study abstracts were screened for eligibility by two reviewers. Following this screening process, full-text articles were reviewed to determine the eligibility of the studies. Eligible studies were then evaluated by coding findings with descriptive labels to distinguish elements that appeared pertinent to this review. Coding was used to form groups, and these groups led to the development of themes. RESULTS: Twenty-five studies met the eligibility criteria for this literature review. The themes identified were work performance, organizational justice, pay, status, personal characteristics, work relationships (including bullying), autonomy, organizational identification, training, and meaningfulness of work. CONCLUSION: Most of the research involved the use of surveys. There is a need for more qualitative research and for the use of case studies to examine work motivation in health care organizations. All of the studies were cross-sectional. Longitudinal research would provide insight into how work motivation changes, and how it can be influenced and shaped. Several implications for practice were identified. There is a need to ensure that health care workers have access to training opportunities, and that autonomy is optimized. To improve work motivation, there is a need to address bullying and hostile behaviours in the workplace. Addressing the factors that influence work motivation in health care settings has the potential to influence the care that patients receive.
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 | 0.017 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".