Incentivizing Rural Work Preferences Among Specialist Physicians: Protocol for a Discrete Choice Experiment
Notice bibliographique
Résumé
BACKGROUND: Retaining specialist physicians in rural parts of India poses a fundamental challenge, which affects the health care system's functionality and provision of standard health care services. There has been an acute shortfall of specialist physicians in the fields of medicine, pediatrics, obstetrics and gynecology, and surgery at rural community health centers. This necessitates urgent policy focus to address the shortages and design effective rural retention strategies. In this study, which uses a discrete choice experiment (DCE), individuals choose from multiple-choice preferences that resemble hypothetical job descriptions. OBJECTIVE: DCEs are a quantitative approach to assessing several aspects of job selection. This study aims to develop a detailed plan of a DCE method used to determine specialist physicians' job choices. This protocol outlines the DCE method, which uses an exploratory sequential mixed methods research design to understand specialist physicians' preferences and design reward packages that would effectively motivate them to work in underserved regions. METHODS: The qualitative phase of the study involved identifying job attributes and their corresponding levels for the DCE. We followed a meticulous process, which included reviewing relevant literature, performing qualitative pilot work, conducting in-depth individual interviews, and consulting with medical and health experts. The quantitative phase involved generating a D-efficient orthogonal fractional factorial design using Ngene software to create choice scenarios using the identified job factors and their corresponding levels. The generated choice scenarios were blocked into 6 versions in 6 blocks. The DCE was undertaken among final-year postgraduate medical residents and specialist physicians from several health care facilities in Rajasthan. Various statistical models will be applied to explore the response variability and quantify the trade-offs that participants are willing to make for nonmonetary features as a substitute for adjustments in the monetary attribute. RESULTS: After the ethics committee's approval of the study, the qualitative data collection phase occurred from September to December 2021, while the quantitative phase took place from May to August 2022. Six attributes and 14 levels were identified and established through qualitative surveys. The experimental design resulted in 36 choice situations, which were grouped into 6 blocks. The preliminary investigation demonstrated that the instrument was valid and reliable. Statistical data analysis has been initiated, and the principal findings are expected to be disseminated in January 2025. CONCLUSIONS: The protocol provides a systematic framework to assess specialist physicians' preferences regarding working in rural health care centers. This research has the potential to substantially influence the future of rural health care by laying the foundation for understanding specialist physicians' choices, which will help design future incentive schemes, policy interventions, and research. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/59621.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,046 | 0,046 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,006 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,062 | 0,008 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».