| Literature DB >> 35996107 |
Jing-Li Yue1, Na Li1, Jian-Yu Que1, Si-Fan Hu1, Na-Na Xiong1, Jia-Hui Deng1, Ning Ma1, Si-Wei Sun1, Rui Chi1, Jie Shi2, Hong-Qiang Sun3.
Abstract
BACKGROUND: High-quality mental health services can improve outcomes for people with mental health problems and abate the burden of mental disorders. We sought to identify the challenges the country's mental health system currently faces and the human resource situation related to psychological services and to provide recommendations on how the mental health workforce situation could be addressed in China.Entities:
Keywords: Capacity; Government policy; Mental health; Psychotherapy; Workforce
Mesh:
Year: 2022 PMID: 35996107 PMCID: PMC9394058 DOI: 10.1186/s12888-022-04204-7
Source DB: PubMed Journal: BMC Psychiatry ISSN: 1471-244X Impact factor: 4.144
Demographic characteristics of participants by geographical region
| Characteristics | Geographical Regions | ||||
|---|---|---|---|---|---|
| Total ( | Eastern area ( | Central area ( | Western area ( | ||
| Workforce types N (%) | < 0.001 | ||||
| Psychiatrist | 1495 (39.1) | 736 (52.5) | 262 (30.0) | 497 (32.0) | |
| Psychotherapist | 263 (6.9) | 133 (9.5) | 66 (7.6) | 64 (4.1) | |
| Counselor | 193 (5.0) | 75 (5.4) | 66 (7.6) | 52 (3.4) | |
| Nurse | 1487 (38.9) | 248 (24.8) | 379 (43.5) | 760 (49.0) | |
| Other | 386 (10.1) | 109 (7.8) | 99 (11.4) | 178 (11.5) | |
| Age N (%) | < 0.001 | ||||
| ≤ 45 | 3178 (83.7) | 1136 (81.3) | 739 (85.4) | 1303 (84.9) | |
| > 45 | 619 (16.3) | 261 (18.7) | 126 (14.6) | 232 (15.1) | |
| Gender N (%) | < 0.001 | ||||
| Male | 1172 (30.6) | 520 (37.1) | 310 (35.6) | 342 (22.1) | |
| Female | 2652 (69.4) | 881 (62.9) | 562 (64.4) | 1209 (77.9) | |
| Education level N (%) | < 0.001 | ||||
| High school or below | 103 (2.7) | 19 (1.4) | 55 (6.3) | 29 (1.9) | |
| Junior college | 1015 (26.5) | 243 (17.3) | 289 (33.1) | 483 (31.1) | |
| Bachelor | 2329 (60.9) | 927 (66.2) | 459 (52.6) | 943 (60.8) | |
| Master | 355 (9.3) | 201 (14.3) | 65 (7.5) | 89 (5.7) | |
| Doctor | 22 (0.6) | 11 (0.8) | 4 (0.5) | 7 (0.5) | |
| Title N (%) | < 0.001 | ||||
| Primary | 1827 (47.8) | 524 (37.4) | 464 (53.2) | 839 (54.1) | |
| Intermediate | 1117 (29.2) | 506 (36.1) | 227 (26.0) | 384 (24.8) | |
| • Subsenior | 476 (12.4) | 225 (16.1) | 75 (8.6) | 176 (11.3) | |
| Senior | 137 (3.6) | 69 (4.9) | 23 (2.6) | 45 (2.9) | |
| Other | 267 (7.0) | 77 (5.5) | 83 (9.5) | 107 (6.9) | |
| Hospital levels N (%) | < 0.001 | ||||
| Tertiary general hospital | 315 (8.2) | 59 (4.2) | 70 (8.0) | 186 (12.0) | |
| Tertiary psychiatric hospital | 1640 (42.9) | 454 (32.4) | 337 (38.6) | 859 (54.7) | |
| Secondary-level general hospital | 394 (10.3) | 164 (11.7) | 126 (14.4) | 104 (6.7) | |
| Secondary-level psychiatric hospital | 1121 (29.3) | 598 (42.7) | 238 (27.3) | 285 (18.4) | |
| Community hospitals | 354 (9.3) | 126 (9.0) | 101 (11.6) | 127 (8.2) | |
Training situation of participants by geographical regions
| Geographical Regions | ||||||
|---|---|---|---|---|---|---|
| Total ( | Eastern area ( | Central area ( | Western area ( | |||
| Short-term training N (%) | Yes | 1919 (50.2) | 750 (53.5) | 452 (51.8) | 717 (46.2) | < 0.001 |
| No | 1905 (49.8) | 651 (46.5) | 420 (48.2) | 834 (53.8) | ||
| Long-term training N (%) | Yes | 988 (25.8) | 395 (28.2) | 255 (29.2) | 338 (21.8) | < 0.001 |
| No | 2836 (74.2) | 1006 (71.8) | 617 (70.8) | 1213 (78.2) | ||
| Self-experience N (%) | Yes | 1210 (31.6) | 423 (30.2) | 314 (36.0) | 473 (30.5) | < 0.001 |
| No | 2614 (68.4) | 978 (69.8) | 558 (64.0) | 1078 (69.5) | ||
| Supervision N (%) | Yes | 1177 (30.8) | 460 (32.8) | 302 (34.6) | 415 (26.8) | < 0.001 |
| No | 2647 (69.2) | 941 (67.2) | 570 (65.4) | 1136 (73.2) | ||
Competence in psychological counseling/psychotherapy by geographical region
| Variable | Geographical Regions | |||
|---|---|---|---|---|
| Eastern area ( | Central area ( | Western area ( | ||
| Psychological competency | 6 (5,8) | 6 (4,8) | 5 (3,7) | < 0.001 |
Multiple linear regression analysis of counseling and psychotherapy competence
| Model 1 | Model 2 | |
|---|---|---|
| Short-term training (Yes/No) | −0.327** | −0.223* |
| Long-term training (Yes/No) | −0.887*** | −0.884*** |
| Self-experience (Yes/No) | − 0.460*** | − 0.586*** |
| Supervision (Yes/No) | −1.362*** | −1.260** |
| Gender (Male/female) | −0.284** | |
| Age (≤ 45/> 45) | 0.293* | |
| Geographical regions | −0.289** | |
| Hospital levels | −0.236*** | |
| Education level | −0.071 | |
| Workforce | −0.190*** | |
| Title | −0.028 | |
| ΔR2 | 0.161 | 0.196 |
| Adjusted R2 | 0.160 | 0.193 |
| 182.102*** | 83.660*** |
*P < 0.05, **P < 0.01, ***P < 0.001; In step 1, training situations were added. In step 2, demographics were added
Subgroup analyses stratified by geographical regions
| Variables | Geographical Regions | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Eastern area | Central area | Western area | |||||||
| β (SE) | 95% CI of β | β (SE) | 95% CI of β | β (SE) | 95% CI of β | ||||
| Short-term training | −0.409 (0.154) | −0.711 ~ − 0.108 | < 0.01 | −0.186 (0.209) | − 0.596 ~ 0.224 | 0.374 | − 0.045 (0.160) | −0.360 ~ 0.269 | 0.777 |
| Long-term training | −0.898 (0.185) | − 1.261 ~ − 0.536 | < 0.001 | − 0.591 (0.244) | −1.070 ~ − 0.112 | < 0.05 | −1.010 (0.202) | − 1.407 ~ − 0.613 | < 0.001 |
| Self-experience | −0.350 (0.167) | − 0.677 ~ − 0.023 | < 0.05 | −0.715 (0.232) | −1.171 ~ − 0.259 | < 0.01 | −0.741 (0.174) | −1.083 ~ − 0.400 | < 0.001 |
| Supervision | −1.068 (0.178) | − 1.418 ~ − 0.719 | < 0.001 | − 1.322 (0.244) | −1.800 ~ − 0.844 | < 0.001 | −1.363 (0.195) | − 1.745 ~ − 0.980 | < 0.001 |
| Gender | −0.462 (0.135) | − 0.726 ~ − 0.198 | < 0.01 | −0.022 (0.183) | − 0.381 ~ 0.338 | 0.906 | − 0.354 (0.167) | −0.681 ~ − 0.026 | < 0.05 |
| Age | 0.461 (0.173) | 0.121 ~ 0.800 | < 0.01 | 0.386 (0.254) | −0.112 ~ 0.885 | 0.129 | −0.034 (0.202) | −0.430 ~ 0.362 | 0.867 |
| Hospital levels | −0.279 (0.060) | −0.397 ~ − 0.161 | < 0.001 | −0.118 (0.075) | − 0.265 ~ 0.029 | 0.115 | − 0.262 (0.060) | −0.379 ~ − 0.145 | < 0.001 |
| Education level | 0.132 (0.114) | −0.091 ~ 0.356 | 0.245 | 0.160 (0.137) | −0.108 ~ 0.428 | 0.241 | −0.445 (0.123) | −0.686 ~ − 0.205 | < 0.001 |
| Workforce | 0.006 (0.048) | −0.087 ~ 0.099 | 0.896 | −0.265 (0.067) | −0.396 ~ − 0.134 | < 0.001 | −0.321 (0.050) | − 0.420 ~ − 0.222 | < 0.001 |
| Title | −0.007 (0.061) | − 0.127 ~ 0.113 | 0.913 | − 0.137 (0.071) | −0.275 ~ 0.002 | 0.054 | 0.042 (0.062) | −0.079 ~ 0.163 | 0.493 |
| ΔR2 | 0.188 | 0.201 | 0.198 | ||||||
| Adjusted R2 | 0.182 | 0.192 | 0.192 | ||||||
| 32.067*** | 21.496*** | 37.543*** | |||||||
*P<0.05, **P<0.01,***P<0.001
Summary of recommendations
| Summary of recommendations | |
|---|---|
| a Recommendations for areas (or hospitals) of deficient workforce like western China (or community hospitals): | |
| a1. Learning task sharing to transfer some mental health care responsibilities from more-specialized to less-specialized staff | |
| b Recommendations for areas (or hospitals) like eastern China (or tertiary hospitals): | |
| b1. The minimum requirements on education and training for psychotherapy workforce (such as at least being graduates of medicine or psychology) are in need to make the specialization in psychotherapy | |
| b2. The measurable and minimum core criteria of psychotherapeutic training about theoretical training, self-experience and supervision should be created locally | |
| c Recommendations for policy or regulations: | |
| c1. The government not only needs to increase the number of mental health workforce but also strengthen the training of psychotherapy to enhance the quality of mental health services | |
| c2. Providing standard clinical training for psychologists and adjusting the policy to making them access to hospital more flexibly could have great potentials to expand psychotherapy-related human resources | |
| c3. Breaking down current policy barriers and establishing novel policy and regulations nationally and locally are imminent for China | |
| d Other potential recommendations: | |
| d1. Digital applications can extend the capacity and reach of the limited number of mental health specialists by facilitating offsite supervision and mentoring of local health and lay providers, reducing regional and hospital imbalances | |
| d2. Online training and the use of peers to supervise therapy quality with structured scales and feedback have a potential for alleviating shortage of experienced trainers |