A phenomenological study on the migration trends of Filipino pharmacists who graduated from a university in Manila
Notice bibliographique
Résumé
Abstract Introduction Migration trends and patterns differ in every country, and the current phenomena are happening at a particular time, whether in retrospect or prospect. In the Philippines, an influx of migration, especially among pharmaceutical healthcare providers, is seen due to the demands in the medical field even before the global COVID-19 pandemic. Aim This study determined the factors that influenced Filipino pharmacists' who graduated from a University in Manila; their motivation to work and migrate to other countries, as well as the responsibilities, competencies, and working conditions of pharmacists waiting for them abroad. Methods Following institutional ethical approval, snowball sampling was employed as the sampling method wherein the research participants were asked to help determine and recruit potential respondents. The sample size used to collate information for this study was 15 respondents from 3 different countries of origin, specifically Canada, Singapore, and the United States of America. Through a semi-structured interview, the participants elicited different responses with common themes analysed through thematic analysis. The respondents were not required to disclose any information they were not allowed to reveal. Hence, the researchers fully understood and did not force the respondents to get the information without the participant’s permission. In cases where participants shared information with another participant, this was their own doing, and researchers could not be responsible for this. The researchers safeguarded the information and identities of each participant as far as reasonably possible. Furthermore, each piece of data given is protected through the Philippine Data Privacy Act of 2012, and researchers are ensured to comply fully with what was written in the Informed Consent Form. Results All fifteen participants took their pharmacy undergraduate degree at a university in Manila before migrating or working abroad. Participants have at least a year of experience working abroad, covering the years 2010-2020, ranging from the completion of four-year pharmacy degrees to participants who graduated with five-year pharmacology degrees. Key themes which influenced the Filipino pharmacists included their chosen pharmacy degrees, including the University's curriculum reliability and relevance, turning point of migration decision, factors of their migration, ways migration was obtained, pharmacy work opportunities abroad, prior pharmacy working experience in the Philippines, work dynamics abroad, and their annual salary with benefits. Discussion/Conclusion There are numerous and limitless opportunities abroad, but most of them differ in the work setting in the Philippines, wherein they are more aligned with the clinical side of being a pharmacist. Moreover, the research field of the profession is still an emerging field with a wide array of opportunities. Overall, the study has collected varying and new insights into Filipino Pharmacists' migration, specifically their migration trends, competencies, responsibilities, and working conditions that may contribute to the betterment of the institution's curriculum and pharmacy practice in the Philippines. With the growing demand for pharmacists in the Philippines, the knowledge gained from a similar study would help improve conditions for healthcare workers to avert their desire or need to migrate. References 1. Loquias, M. and Robles, Y. Issues and Concerns on Utilization of the Pharmacy Workforce in the Philippines. [Internet]. 2012 [cited 2021 Sept 10]; JAASP 2012;1(2):86-96. Available from https://www.aaspjournal.org/uploads/155/5955_pdf.pdf 2. Lorenzo, F., et. al. Migration of Health Workers: Country case study Philippines. ILO. [Internet] 2006. [cited 2021 Oct 15] Available from: https://www.ilo.org/wcmsp5/groups/public/---ed_dialogue/---sector/documents/publication/wcms_161163.pdf 3. Morii, Y., Furuta, S., Ishikawa, T., Fujiwara, K., Yamashina, H., & Ogasawara, K. Projecting supply and demand for pharmacists in pharmacies based on the number of prescriptions and system dynamics modeling. Human Resources for Health, 18(1). [Internet]. 2020 [cited 2021 Sept 27]; Available from: https://doi.org/10.1186/s12960-020-00524-5
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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 | 0,005 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,010 | 0,005 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».