Factors influencing practitioners’ who do not participate in ethically complex, legally available care: scoping review
Bibliographic record
Abstract
BACKGROUND: Evolving medical technology, advancing biomedical and drug research, and changing laws and legislation impact patients' healthcare options and influence healthcare practitioners' (HCPs') practices. Conscientious objection policy confusion and variability can arise as it may occasionally be unclear what underpins non-participation. Our objective was to identify, analyze, and synthesize the factors that influenced HCPs who did not participate in ethically complex, legally available healthcare. METHODS: We used Arksey and O'Malley's framework while considering Levac et al.'s enhancements, and qualitatively synthesized the evidence. We searched Medline, CINAHL, JSTOR, EMBASE, PsychINFO, Sociological Abstracts, and ProQuest Dissertations and Theses Global from January 1, 1998, to January 15, 2020, and reviewed the references of the final articles. We included articles written in English that discussed the factors that influenced physicians and registered nurses (RNs) who did not participate in end-of-life (EOL), reproductive technology and health, genetic testing, and organ or tissue donation healthcare areas. Using Covidence, we conducted title and abstract screening, followed by full-text screening against our eligibility criteria. We extracted the article's data into a spreadsheet, analyzed the articles, and completed a qualitative content analysis using NVivo12. RESULTS: We identified 10,664 articles through the search, and after the screening, 16 articles were included. The articles sampled RNs (n = 5) and physicians (n = 11) and encompassed qualitative (n = 7), quantitative (n = 7), and mixed (n = 2) methodologies. The care areas included reproductive technology and health (n = 11), EOL (n = 3), organ procurement (n = 1), and genetic testing (n = 1). One article included two care areas; EOL and reproductive health. The themed factors that influenced HCPs who did not participate in healthcare were: (1) HCPs' characteristics, (2) personal beliefs, (3) professional ethos, 4) emotional labour considerations, and (5) system and clinical practice considerations. CONCLUSION: The factors that influenced HCPs' who did not participate in ethically complex, legally available care are diverse. There is a need to recognize conscientious objection to healthcare as a separate construct from non-participation in healthcare for reasons other than conscience. Understanding these separate constructs will support HCPs' specific to the underlying factors influencing their practice participation.
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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 | 0.044 | 0.742 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.040 |
| Insufficient payload (model declined to judge) | 0.018 | 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; both teacher heads agree on what is shown here.
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".