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Record W4393469964 · doi:10.1111/hex.14036

Factors affecting patients' journey with primary healthcare services during mental health‐related sick leave

2024· article· en· W4393469964 on OpenAlexafffundabout
Justine Labourot, Émilie Pinette, Nadia Giguère, Matthew Menear, Cynthia Cameron, Élyse Marois, Brigitte Vachon

Bibliographic record

VenueHealth Expectations · 2024
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsUniversité LavalUniversité de MontréalInstitut universitaire en santé mentale de MontréalInstitut Universitaire en Santé Mentale de Québec
FundersFonds de Recherche du Québec - SantéUniversité de Montréal
KeywordsPsychological interventionMental healthSick leaveContext (archaeology)Health careAutonomyMedicineOpenness to experienceNursingAnxietyPsychologyRehabilitationQualitative researchPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

CONTEXT: Best practice guidelines for the recovery and return to work (RTW) of people with mental disorders recommend access to the services of an interdisciplinary team combining pharmacological, psychological and work rehabilitation interventions. In the Canadian context, primary healthcare services are responsible for providing these services for people with common mental disorders, such as depressive or anxiety disorders. However, not everyone has easy access to these recommended primary healthcare services, and previous studies suggest that multiple personal, practice-related and organizational factors can influence the patient's journey. Moreover, previous studies documented that family physicians often work in silos and lack the knowledge and time needed to effectively manage by themselves patients' occupational health. Thus, the care and service trajectories of these patients are often suboptimal and can have important consequences on the person's recovery and RTW. OBJECTIVE AND POPULATION STUDIED: Our study aimed to gain a better understanding of the patient journeys and the factors influencing their access to and experience with primary healthcare services while they were on sick leave due to a common mental disorder. METHODS: A descriptive qualitative research design was used to understand and describe these factors. Conventional content analysis was used to analyze the verbatim. RESULTS: Five themes describe the main factors that influenced the patient's journey of the 14 participants of this study: (1) the fragmented interventions provided by family physicians; (2) patients' autonomy in managing their own care; (3) the attitude and case management provided by the insurer, (4) the employer's openness and understanding and (5) the match between the person's needs and their access to psychosocial and rehabilitation services. CONCLUSIONS: Our findings highlight important gaps in the collaborative practices surrounding the management of mental health-related sick leave, the coordination of primary healthcare services and the access to work rehabilitation services. Occupational therapists and other professionals can support family physicians in managing sick leaves, strengthen interprofessional and intersectoral collaboration and ensure that patients receive needed services in a timelier manner no matter their insurance coverage or financial needs. PATIENTS OF PUBLIC CONTRIBUTION: This study aimed at looking into the perspective of people who have lived or are currently experiencing a sick leave related to a mental health disorder to highlight the factors which they feel hindered their recovery and RTW. Additionally, two patient partners were involved in this study and are now engaged in the dissemination of the research results and the pursuit of our team research programme to improve services delivered to this population.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.277
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0070.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.360
Teacher spread0.334 · 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 teacher head, not a consensus.

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

Citations3
Published2024
Admission routes3
Has abstractyes

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