Investigation of Factors That May Affect the Commitment of Healthcare Professionals to Their Works During the COVID-19 Pandemic Period
Bibliographic record
Abstract
The environment of uncertainty created by the COVID-19 pandemic period has caused difficulties especially for healthcare professionals in their work activities. The purpose of this research is to find out which variables might affect the commitment of healthcare professionals to their works during this COVID-19 period. Based on the data announced by the Ministry of Health during the pandemic in the first quarter of 2021, it was decided to conduct a research on doctors, nurses, caregivers, and medical secretaries working in hospitals in the cities of the Eastern Black Sea Region of Turkey which generally show high risk. In the developed research model, satisfactory conditions (SC), emotional commitment to change (ECC), and psychological ownership (PO) as variables that may directly or indirectly affect the commitment of healthcare professionals to their works (CW) were used. The Smart PLS program was used in the analysis of the research model and hypothesis. It was seen that the ECC of healthcare professionals has a positive and significant (.694; p < .000) effect on SC. It was understood that the PO of healthcare professionals has positive and significant effects on their CW (.394; p < .000). It was also observed that the presence of SC has positive and significant effects on the PO of the healthcare professionals in the current situation (.796; p < .000). It was observed that only the effect of ECC of healthcare professionals on their CW is insignificant (.097; p > .086). Looking at the indirect (intermediary) effects obtained as a result of the research, it was seen that all of the hypotheses consist of positive coefficients. This situation reveals that the mediating variables have complementary effects on the obtained results.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".