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Enregistrement W4396845137 · doi:10.63050/jpps.19.03.195

STUDENTS HEALTH AND WELL-BEING PROJECT, PAKISTAN

2022· article· en· W4396845137 sur OpenAlexaboutno aff
Muhammad Tahir Abbasi, Sehar Rameez, Ayesha Tahir, R. Anggi Apriyani Usman, Alia Islam, Munazza

Notice bibliographique

RevueJournal of Pakistan Psychiatric Society · 2022
Typearticle
Langueen
DomainePsychology
ThématiqueCOVID-19 and Mental Health
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMathematics educationPsychology

Résumé

récupéré en direct d'OpenAlex

Background: Most undergraduate students in low-middle-income countries fall into a vulnerable age group between 19 and 24 years, where the initial onset of mental illnesses is most common. Additionally, students face elevated levels of stress, financial difficulties, increased autonomy, and social pressures and interact more frequently with social media and technology, which can hinder self-esteem and feelings of inclusion (Caitlin Monaghan, 2020). In the last two years, young students' mental health and well-being challenges have escalated worldwide due to the mental health impacts of COVID-19. This is an important challenge faced by global public health, as mental health is foundational to academic success, employment outcomes, and the formation of personal relationships. In Pakistan, approximately 64% of young people are under the age of 30 (Imran, 2018). More than 90 million Pakistanis are suffering from physiological and mental illnesses, and most of them are young people. Unfortunately, mental health issues are a highly neglected area in LMICs, like Pakistan. Thus, the youth in the country are facing numerous mental issues that are destroying their physical health, leading to increased substance use and suicidal deaths (Imran, 2018). Suggested implementation framework and strategy for improving mental health in Pakistani youth: While there have been several initiatives and tools released by global organizations over the past several years designed to address post-secondary mental health, there is currently no overarching national policy or guiding framework to steer action related to post-secondary mental health in Pakistan. We suggest local delivery of a customized mental health framework to Pakistani institutions to create a long-lasting impact on youth mental health. Proposed actions of Project SHAW PRISM: SHAW- Pakistan will be working with health agencies, national nongovernmental organizations (NGOs), educational institutes, health organizations, and youth-serving organizations to promote collaboration and reduce negative health outcomes by creating concrete steps for educational institutions, to play a vital role in promoting youth mental health by: Creating a framework of connectedness between educational institutions, faculties, families, and healthcare providers through an effective digital health Supporting educational institutions in the implementation of health prevention strategies, policies, and Providing tools and resources for universities to help promote the importance of early prevention of mental and physical health Institutional Benefits: Some highlights of how the institutions will be benefitted from SHAW-Pakistan are: Provide professional development on classroom management. Faculty members can reinforce positive health behaviors in classrooms and referrals to relevant services after receiving training programs from SHAW–PRISM. SHAW- PRISM will support student-led clubs at school. These clubs will create a safe space for students to socialize, support each other, and connect with supportive health Facilitate positive youth development Implementing mentoring programs, providing opportunities to volunteer in the community; or connecting students to community-based programs can provide youth with a network of supportive adults. Provide faculty, students, and their parents and families with resources that support positive healthy lifestyle Reduce stigma by creating dialogue such as open, honest communication and empathy. SHAW- Digital Healthcare Providers will: Ask about family relationships, PTDS, school experiences, etc as a part of routine health Encourage the early prevention of mental and physical health problems by referral to available health programs Promote prevention interventions over treatment Engage parents in discussions about how to connect with their adolescents, communicate effectively, and monitor activities and health behaviors. Educate parents and youth about adolescent development and health risks. Expected Health Outcomes: We aim to assess the effectiveness of a customized step-wise approach to the nationwide implantation of the Mental Health Framework in Pakistani educational institutes, to promote mental health literacy at a vulnerable life stage for the prevention of future mental health disorders and substance uptake and also reduce the risk factors associated with it. Promote digital mental health services for students in Pakistan and investigate the process and outcomes. By sharing the knowledge with local stakeholders and policymakers, we intend to create a long-lasting impact and advocate for introducing mandatory mental health services in Pakistani educational institutions. Reduce the noncommunicable disease burden in the country by helping to create healthier doctors who know and practice a healthier lifestyle and will, in turn, transfer their skills and knowledge to their patients. Thereby, promoting population health and reducing the burden of not only mental health issues but also other NCDs that are invariably linked to poor mental health and lifestyle choices. Create a continuous knowledge translation and sustainability of this project by preparing future doctors in mental health prevention strategies, mental health literacy, introducing digital mental health navigation tools for existing services, reduction of stigma, and creating compassion in medical professionals. Highlight the impact of implementation outcomes at the health policy level for a mandatory recommendation of mental health and wellness services in the region. REFERENCES: Caitlin Monaghan, B. L. (2020). Postsecondary Mental Health Policy in Canada: A Scoping Review of the Grey Literature: Politique de santé mentale post-secondaire au Canada: un examen de la portée de la littérature grise. The Canadian Journal of Psychiatry. Chai, C. (2013, April 8). Healthy doctors make healthy patients, a Canadian study finds. Retrieved from Global News: https://globalnews.ca/news/462960/healthy-doctors-make-healthy-patients-canadian-study-finds/ Oberg, E.B. Frank, E ( 2009). Physicians’ health practices strongly influence patient health practices. J R Coll Physicians Edinb. 39:290–1. doi:10.4997/JRCPE.2009.422 Global Alliance for Chronic Diseases. (n.d.). Retrieved from Future call topics: https://www.gacd.org/funding/gacd-announces-topics-for- 2021-2023-funding-calls Imran, M. (2018). Psychological issues among youth reach dangerous level. Retrieved from The News: https://www.thenews.com.pk/print/302175-psychological-issues-among-youth-reach-dangerous- level#:~:text=Talking%20to%20'The%20News'%2C,in%20the%20upper%20economic%20strata. Mental Health Commission of Canada. (2021). The National Standard for Mental Health and Well-Being-FAQs. Retrieved from MHCC: https://www.mentalhealthcommission.ca/wp-content/uploads/drupal/2021-07/student_standard_faq_2020_eng_0.pdf OCAD U and Ryerson University Campus. (2014, April). POLICY APPROACHES TO POST-SECONDARY STUDENTS. Retrieved from OCAD U and Ryerson University Campus: https://campusmentalhealth.ca/wp-content/uploads/2018/04/Policy-Approaches-to-PS-student- MH.FINAL_April15-2014.pdf Research Gate. (n.d.). Healthy Doc=Healthy patients Projects. Retrieved from Research Gate: https://www.researchgate.net/project/Healthy- Doc-Healthy-Patient

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,357
Score d'incertitude au seuil0,856

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,028
Tête enseignante GPT0,441
Écart entre enseignants0,413 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2022
Routes d'admission1
Résumé présentoui

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