Grace Shen-Tu
Biographic Data
| ID | 831199 |
|---|---|
| NAME | Grace Shen-Tu |
| GIVEN NAMES | Grace |
| FAMILY NAME | Shen-Tu |
| SIGNATURE | SHEN-TU G |
| AFFILIATIONS | Alberta’s Tomorrow Project, Calgary, AB, Canada |
| VERIFIED | No |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 2 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 1 |
Combining Text Mining and Manual Thematic Analysis to Understand Participant Experiences With Surveys
Participant engagement in large-scale surveys is vital to health and social science research. Understanding the factors that affect participant engagement requires methods that can meaningfully interpret participant feedback. Open-ended survey questions can capture these kinds of nuanced responses, but can be challenging to analyze at scale. To address this challenge, this study combines computational text mining with a manual thematic analysis t…
Factors associated with mental health service use during the pandemic
Background: Scarce are the studies focusing on initiation of new mental health service use (MHSU) and distinguishing individuals who have sought services but have been unsuccessful in accessing these. Aims: Assessing the factors associated with initiating new MHSU as compared to no MHSU due to self-reported no need, no MHSU due to health system and personal barriers and MHSU using resources already in place. Methods: The sample included participa…
Factors associated with change in moderate or severe symptoms of anxiety and depression in community-living adults and older adults during the Covid-19 pandemic
Health and socio-economic factors were associated with changes in symptoms of anxiety and depression during the pandemic, further increasing inequities in mental health needs. Public health campaigns on the importance of healthy behaviours should continue and health policies should reduce economic and social barriers to integrated substance use and mental health care
Combining Text Mining and Manual Thematic Analysis to Understand Participant Experiences With Surveys
Participant engagement in large-scale surveys is vital to health and social science research. Understanding the factors that affect participant engagement requires methods that can meaningfully interpret participant feedback. Open-ended survey questions can capture these kinds of nuanced responses, but can be challenging to analyze at scale. To address this challenge, this study combines computational text mining with a manual thematic analysis t…
Factors associated with change in moderate or severe symptoms of anxiety and depression in community-living adults and older adults during the Covid-19 pandemic
Health and socio-economic factors were associated with changes in symptoms of anxiety and depression during the pandemic, further increasing inequities in mental health needs. Public health campaigns on the importance of healthy behaviours should continue and health policies should reduce economic and social barriers to integrated substance use and mental health care
Factors associated with mental health service use during the pandemic
Background: Scarce are the studies focusing on initiation of new mental health service use (MHSU) and distinguishing individuals who have sought services but have been unsuccessful in accessing these. Aims: Assessing the factors associated with initiating new MHSU as compared to no MHSU due to self-reported no need, no MHSU due to health system and personal barriers and MHSU using resources already in place. Methods: The sample included participa…
Combining Text Mining and Manual Thematic Analysis to Understand Participant Experiences With Surveys
Participant engagement in large-scale surveys is vital to health and social science research. Understanding the factors that affect participant engagement requires methods that can meaningfully interpret participant feedback. Open-ended survey questions can capture these kinds of nuanced responses, but can be challenging to analyze at scale. To address this challenge, this study combines computational text mining with a manual thematic analysis t…
Anxiety (2 works) · COVID-19 and Mental Health (2 works) · Environmental health (2 works) · Gerontology (2 works) · Gerontology (2 works) · Medicine (2 works) · Mental health (2 works) · Psychiatry (2 works) · Psychology (2 works) · Cohort (1 work)