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Record W4390945189 · doi:10.5334/ijic.icic23083

Engaging and Empowering Young Carers: Shifting the Focus for Public Awareness and Intervention in Canada

2023· article· en· W4390945189 on OpenAlexaffabout
Marianne Saragosa, Karen Okrainec, Shoshana Hahn‐Goldberg, Isabelle Caven, Yuanjie Zheng, Amanda Ramkishun, Georgia Pomozova-Mann

Bibliographic record

VenueInternational Journal of Integrated Care · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsUniversity of TorontoUniversity Health NetworkSinai Health System
Fundersnot available
KeywordsAcknowledgementFocus groupIntervention (counseling)NursingParticipatory action researchHealth carePsychologyFamily memberMedicineFamily medicine

Abstract

fetched live from OpenAlex

Over 8 million Canadians provide care to chronically ill or disabled family members. While most caregivers are between the ages of 45 and 54 (24%) and 55 and 64 years (20%), the third largest group is young carers. Carers between the ages of 15 and 24 account for 15% of all caregivers in Canada. This group of carers are those under the age of 25 that provide significant and ongoing unpaid care to a family member or friend. However, despite these known figures, very little attention has been paid to the existence of young carers in Canada except for scant emerging research. Our two-phased study aimed to first gain a better understanding of how young carers interact with the health system [how they are included in discussions and how is information shared with young carers]. The second phase focused on building on this body of knowledge by engaging young people and partnering with caregiver organizations in a more participatory, active manner to identify potential solutions to improve their involvement in supporting their family member’s healthcare use. In phase one, we applied a deductive analytical approach to identify touch points and pain points as perceived by participants. Our findings demonstrated that young carers interacted with the healthcare system at touch points spanning the home and community sectors, hospital and rehabilitation care, and primary and palliative care services. We also identified five types of pain paints that the young carer participants experienced in their caregiving roles: 1) desiring acknowledgement; 2) seeking information and communication; 3) managing system navigation; 4) engaging in balancing acts; and 5) performing point-of-care tasks. Phase two consisted of a virtual co-design event with the young carer participants, members of the partnering support organizations, and the research team. During the meeting, the group engaged in a brainstorming activity to identify priorities for our proposed project. The two priorities identified by our event were to 1) Build awareness of young carers for both healthcare organizations and healthcare practitioners and identify challenges in their recognition and support at point-of-care. And 2) Support the co-design of a prototype program that recognizes the needs of both young carers and healthcare workers during their healthcare interactions and that addresses the identified pain points. These results will inform the next steps that intend to explore to what extent are health care professionals aware of young carers and their support needs. Our program of research is adding to the national literature on Canadian young carers and provides more evidence to develop support provisions and interventions for young carers.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.898

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0190.005
Scholarly communication0.0040.002
Open science0.0030.007
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.029
GPT teacher head0.324
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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