| Literature DB >> 25995192 |
Jacqueline Tunnecliff1, Dragan Ilic, Prue Morgan, Jennifer Keating, James E Gaida, Lynette Clearihan, Sivalal Sadasivan, David Davies, Shankar Ganesh, Patitapaban Mohanty, John Weiner, John Reynolds, Stephen Maloney.
Abstract
BACKGROUND: Establishing and promoting connections between health researchers and health professional clinicians may help translate research evidence to clinical practice. Social media may have the capacity to enhance these connections.Entities:
Keywords: communication; eLearning; evidence-based medicine; social media
Mesh:
Year: 2015 PMID: 25995192 PMCID: PMC4468567 DOI: 10.2196/jmir.4347
Source DB: PubMed Journal: J Med Internet Res ISSN: 1438-8871 Impact factor: 5.428
Demographic details of participants.
| Australia, n (%)a | India, n (%) | Malaysia, n (%) | Other, n (%) | Total, n (%) | ||
| Total participants by country | 542 (63.3) | 166 (19.4) | 90 (10.5) | 58 (6.8) | 856 (100) | |
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| Clinicianb | 366 (42.8) | 71 (8.3) | 67 (7.8) | 32 (3.7) | 536 (62.6) |
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| Researcher | 76 (8.9) | 30 (3.5) | 14 (1.6) | 7 (0.8) | 127 (14.8) |
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| Multiplec | 76 (8.9) | 61 (7.1) | 4 (0.5) | 13 (1.5) | 154 (18.0) |
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| Not stated | 24 (2.8) | 4 (0.5) | 5 (0.6) | 6 (0.7) | 39 (4.6) |
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| Undergraduate | 220 (25.7) | 10 (1.2) | 56 (6.5) | 11 (1.3) | 297 (34.7) |
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| Medicine | 134 (15.7) | 28 (3.3) | 51 (6.0) | 10 (1.2) | 223 (26.1) |
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| Allied Healthd | 201 (23.5) | 78 (9.1) | 6 (0.7) | 10 (1.2) | 295 (34.5) |
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| Nursing | 28 (3.3) | 1 (0.1) | 0 (0.0) | 7 (0.8) | 36 (4.2) |
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| Medical Research | 54 (6.3) | 4 (0.5) | 8 (0.9) | 4 (0.5) | 70 (8.2) |
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| Othere | 104 (12.1) | 50 (5.8) | 14 (1.6) | 20 (2.3) | 188 (22.0) |
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| Not stated | 21 (2.5) | 5 (0.6) | 11 (1.3) | 7 (0.8) | 44 (5.1) |
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| ≤24 | 208 (24.3) | 16 (1.9) | 53 (6.2) | 9 (1.1) | 286 (33.4) |
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| 25-34 | 137 (16.0) | 105 (12.3) | 21 (2.5) | 13 (1.5) | 276 (32.2) |
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| 35-44 | 88 (10.3) | 34 (4.0) | 10 (1.2) | 13 (1.5) | 145 (16.9) |
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| 45-54 | 66 (7.7) | 8 (0.9) | 5 (0.6) | 10 (1.2) | 89 (10.4) |
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| 55-64 | 34 (4.0) | 3 (0.4) | 1 (0.1) | 12 (1.4) | 50 (5.8) |
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| 65+ | 8 (0.9) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 8 (0.9) |
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| Not stated | 1 (0.1) | 0 (0.0) | 0 (0.0) | 1 (0.1) | 2 (0.2) |
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| Male | 168 (19.6) | 109 (12.7) | 33 (3.9) | 21(2.5) | 331(38.7) |
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| Female | 372 (43.5) | 56 (6.5) | 57 (6.7) | 37 (4.3) | 522 (61.0) |
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| Not stated | 2 (0.2) | 1 (0.1) | 0 (0.0) | 0 (0.0) | 3 (0.4) |
aAll percentages are based on the total number of participants.
bThe clinician category includes health practitioners in the professional disciplines registered by AHPRA and undergraduate students in those disciplines involved in clinical care.
cThe multiple role category includes participants who identify as a clinician and researcher, or who have other roles in addition to clinician or researcher.
dThe definition of Allied Health for this study is health care professions registered with AHPRA, excluding medicine and nursing.
eIncludes responses where area of practice stated but discipline was unclear.
Figure 1Categories of professional use of social media nominated by respondents.
Frequency of use of social media for professional purposes (the percentage shown is the percent of respondents for each row).
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| Never, n (%) | Less than once month, n (%) | A few times per month, n (%) | A few times per week, n (%) | About once per day, n (%) | More once per day, n (%) | χ2 | DF |
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| Australia | 63 (12.3) | 41 (8.0) | 121 (23.6) | 165 (32.2) | 75 (14.7) | 47 (9.2) | 63.76 | 3 | <.001 |
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| India | 8 (5.0) | 4 (2.5) | 25 (15.7) | 47 (29.6) | 39 (24.5) | 36 (22.7) |
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| Malaysia | 4 (4.5) | 1 (1.1) | 7 (7.9) | 28 (31.5) | 26 (29.2) | 23 (25.8) |
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| Other | 13 (24.1) | 5 (9.3) | 11 (20.4) | 9 (16.7) | 5 (9.3) | 11 (20.4) |
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| <25 | 18 (6.5) | 9 (3.3) | 51 (18.4) | 96 (34.7) | 64 (23.1) | 39 (14.1) | 20.1 | 4 | <.001 |
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| 25-34 | 29 (11.2) | 16 (6.2) | 52 (20.2) | 70 (27.1) | 48 (18.6) | 43 (16.7) |
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| 35-44 | 18 (13.0) | 10 (7.2) | 29 (20.9) | 47 (33.8) | 18 (13.0) | 17 (12.2) |
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| 45-54 | 15 (17.2) | 8 (9.2) | 20 (23.0) | 24 (27.6) | 11 (12.6) | 9 (10.3) |
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| 55+ | 8 (15.1) | 8 (15.1) | 11 (20.8) | 12 (22.6) | 4 (7.6) | 10 (18.9) |
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| Male | 33 (10.7) | 11 (3.6) | 61 (19.7) | 90 (29.1) | 60 (19.4) | 54 (17.5) | 4.57 | 1 | .03 |
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| Female | 55 (10.9) | 40 (7.9) | 103 (20.4) | 158 (31.4) | 85 (16.9) | 63 (12.5) |
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| Undergraduate | 16 (5.5) | 9 (3.1) | 45 (15.5) | 116 (39.9) | 66 (22.7) | 39 (13.4) | 18.46 | 1 | <.001 |
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| Postgraduate / non student | 72 (13.7) | 42 (8.0) | 119 (22.7) | 134 (25.5) | 79 (15.1) | 79 (15.1) |
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Contribution to online material for professional purposes by age.
| Agea | I read online material only | I contribute smallb amounts to online material | I contribute large amounts to online material | ||||||
| n | % age groupc | % total participants | n | % age group | % total participants | n | % age group | % total participants | |
| <25 | 173 | 63.6 | 20.2 | 86 | 31.6 | 10.0 | 13 | 4.8 | 1.5 |
| 25-34 | 132 | 54.3 | 15.4 | 84 | 34.6 | 9.8 | 27 | 11.1 | 3.2 |
| 35-44 | 70 | 56.0 | 8.2 | 45 | 36.0 | 5.3 | 10 | 8.0 | 1.2 |
| 45-54 | 39 | 48.8 | 4.6 | 32 | 40.0 | 3.7 | 9 | 11.3 | 1.1 |
| 55+ | 30 | 60.0 | 3.5 | 17 | 34.0 | 2.0 | 3 | 6.0 | 0.4 |
an=2 did not provide age and are not included in the analysis.
bParticipants were not given a specific definition of a “small amount” or “large amount”.
cPercent of age group that reported interacting with online material.
Figure 2Obstacles to obtaining or sharing research or clinical information nominated by respondents.